SlideShare une entreprise Scribd logo

CNN Attention Networks

•
•
T
Taeoh Kim

Slides for PR-163: CNN Attention Networks

Ingénierie
1  sur  78
Télécharger pour lire hors ligne
CNNAttention-basedNetworks + Attention,CNNReview Tensorflow-KR PR-163,TaeohKim MVPLAB,YonseiUniv
Contents PR-163: CNN_Attention_Networks • Attention, Self-Attention in NLP • CNN-Review • CNN Attention Networks for Recognition • CNN Attention Networks for Other Vision Tasks
Review 1: Attention
Neural Networks PR-163: CNN_Attention_Networks Input Feature Hidden State Representation Prediction
Attention PR-163: CNN_Attention_Networks Quary{Key, Value} {Key, Value} {Key, Value} {Key, Value} {Key, Value} {Key, Value} {Key, Value} {Key, Value} 𝑦 = ෍ 𝑖 𝑤𝑖 𝑥𝑖 𝑦 = ෍ 𝑖 𝑓 𝑄𝑢𝑎𝑟𝑦, 𝐾𝑒𝑦 × 𝑉𝑎𝑙𝑢𝑒 Output
Fully Connected Neural Network PR-163: CNN_Attention_Networks Input Feature Hidden State Representation 𝑤1 𝑤2 … 𝑤 𝑛 Fully Connected NN Represent Blue using Weighted Sum of Inputs without Constraint
Convolutional Neural Network PR-163: CNN_Attention_Networks Input Feature Hidden State Representation (V) (V) (V) (V) (V) (V) 𝑤1 𝑤2 𝑤3 Convolutional NN Represent Blue using Weighted Sum of Inputs from Current Position Q
Attention PR-163: CNN_Attention_Networks Input Feature Hidden State Representation (K, V) (K, V) (K, V) (K, V) (K, V) (K, V) 𝑤1 𝑤2 … 𝑤 𝑛 Attention Represent Blue using Weighted Sum of Inputs from Blue State and Inputs 𝑤 𝑛 = 𝑠𝑜𝑓𝑡𝑚𝑎𝑥 𝑓 𝐵𝑆, 𝐾 𝑛
ex) Machine Translation (PR-055) PR-163: CNN_Attention_Networks Input Feature Hidden State Representation (K, V) (K, V) (K, V) (K, V) (K, V) (K, V) 𝑤1 𝑤2 … 𝑤 𝑛 𝑤 𝑛 = 𝑠𝑜𝑓𝑡𝑚𝑎𝑥 𝑓 𝐵𝑆, 𝐾 𝑛 𝐸𝑛𝑔𝑙𝑖𝑠ℎ 𝐾𝑜𝑟𝑒𝑎𝑛1 𝐾𝑜𝑟𝑒𝑎𝑛2
ex) Image Captioning PR-163: CNN_Attention_Networks Input Feature Hidden State Representation (K, V) (K, V) (K, V) (K, V) (K, V) (K, V) 𝑤1 𝑤2 … 𝑤 𝑛 𝑤 𝑛 = 𝑠𝑜𝑓𝑡𝑚𝑎𝑥 𝑓 𝐵𝑆, 𝐾 𝑛 𝐼𝑚𝑎𝑔𝑒 𝐹𝑒𝑎𝑡𝑢𝑟𝑒 𝐶𝑎𝑝𝑡𝑖𝑜𝑛1 𝐶𝑎𝑝𝑡𝑖𝑜𝑛2
Self-Attention PR-163: CNN_Attention_Networks Input Feature Hidden State Representation (K, V) (K, V) (K, V) (K, V) (K, V) (K, V) 𝑤1 𝑤2 … 𝑤 𝑛 Self-Attention Represent Blue using Weighted Sum of Inputs from Input and Inputs 𝑤 𝑛 = 𝑠𝑜𝑓𝑡𝑚𝑎𝑥 𝑓 𝐾2, 𝐾 𝑛
ex) Transformer (Enc) (PR-049, PR-161) PR-163: CNN_Attention_Networks Input Feature Hidden State Representation (K, V) (K, V) (K, V) (K, V) (K, V) (K, V) 𝑤1 𝑤2 … 𝑤 𝑛 𝑤 𝑛 = 𝑠𝑜𝑓𝑡𝑚𝑎𝑥 𝑓 𝐾2, 𝐾 𝑛 𝐸𝑛𝑔𝑙𝑖𝑠ℎ 𝐸𝑛𝑔𝑙𝑖𝑠ℎ 𝐹𝑒𝑎𝑡𝑢𝑟𝑒
ex) Transformer (Dec) PR-163: CNN_Attention_Networks Input Feature Hidden State Representation (K, V) (K, V) (K, V) (K, V) (K, V) (K, V) 𝑤1 𝑤2 … 𝑤 𝑛 𝑤 𝑛 = 𝑠𝑜𝑓𝑡𝑚𝑎𝑥 𝑓 𝐾2, 𝐾 𝑛 𝐾𝑜𝑟𝑒𝑎𝑛1:𝑛 𝐸𝑛𝑔𝑙𝑖𝑠ℎ 𝐹𝑒𝑎𝑡𝑢𝑟𝑒 (K, V) 𝐾𝑜𝑟𝑒𝑎𝑛 𝑛+1 𝑤1 𝑤2 … 𝑤 𝑛 𝑤 𝑛 = 𝑠𝑜𝑓𝑡𝑚𝑎𝑥 𝑓 𝐾2, 𝐸 𝑛
CNN Self-Attention for Image = Representation PR-163: CNN_Attention_Networks Input Feature Hidden State Representation (K, V) (K, V) (K, V) (K, V) (K, V) (K, V) 𝑤1 𝑤2 … 𝑤 𝑛 𝑤 𝑛 = 𝑠𝑜𝑓𝑡𝑚𝑎𝑥 𝑓 𝐾2, 𝐾 𝑛 𝐼𝑚𝑎𝑔𝑒 𝑅𝑒𝑐𝑜𝑔𝑛𝑖𝑡𝑖𝑜𝑛
ex) Non-local Neural Networks (CVPR18, PR-083) PR-163: CNN_Attention_Networks 𝑦𝑖 = 1 σ 𝑗 exp(𝜃(𝑥𝑖 𝑇 ) ∙ 𝜙(𝑥𝑗)) ෍ 𝑗 exp(𝜃(𝑥𝑖 𝑇 ) ∙ 𝜙(𝑥𝑗))𝑊𝑔 𝑥𝑗 Reshape 1x1Conv Operation Soft max HxWxC HWxC HWxC CxHW HWxHW HWxC Quary Key Value HxWxC + HxWxC
CNN Simplified-Attention for Image = Recalibration PR-163: CNN_Attention_Networks Input Feature Hidden State Representation (K, V) (K, V) (K, V) (K, V) (K, V) (K, V) 𝑤2 𝑤2 = 𝑠𝑖𝑔𝑚𝑜𝑖𝑑 𝑓 𝐾2, 𝐾 𝑛 𝐼𝑚𝑎𝑔𝑒 𝑅𝑒𝑐𝑜𝑔𝑛𝑖𝑡𝑖𝑜𝑛
CNN Simplified-Attention for Image = Recalibration PR-163: CNN_Attention_Networks x
ex) Squeeze-and-Excitation Networks (CVPR18) PR-163: CNN_Attention_Networks
Summary PR-163: CNN_Attention_Networks Attention Quary Structure Objective Examples Attention Current States Recurrent Representation NMT, Captioning, VQA Self-Attention Input Itself Feed-Forward Representation Transformer Non-local NN Input Itself Feed-Forward Recalibration SE-Net, RAN, CBAM
Review 2: CNN Networks
CNN Review PR-163: CNN_Attention_Networks AlexNet 2012 / 39646 VGG 2014.09 / 22554 GoogleNet 2014.09 / 13233 ResNet 2015.12 / 21871 DenseNet 2016.08 / 3591 ResNeXt 2016.11 / 913 Xception 2016.10 / 847 MobileNet 2017.04 / 1553 ShuffleNet 2017.07 / 407 WideResNet 2016.05 / 1063 SE-Net 2017.09 / 724 Residual Attention Net 2017.04 / 257 Non-local Network 2017.11 / 240 CBAM 2018.07 / 54 GCNet 2019.04 / - ResNetv2 2016.03 / 1926 Inception-2,3 2015.12 / 3752 Inception-4 Inception-ResNet 2016.02 / 2152 NASNet 2017.07. / 417
CNN Review PR-163: CNN_Attention_Networks AlexNet 2012 / 39646 VGG 2014.09 / 22554 GoogleNet 2014.09 / 13233 ResNet 2015.12 / 21871 DenseNet 2016.08 / 3591 ResNeXt 2016.11 / 913 Xception 2016.10 / 847 MobileNet 2017.04 / 1553 ShuffleNet 2017.07 / 407 WideResNet 2016.05 / 1063 SE-Net 2017.09 / 724 Residual Attention Net 2017.04 / 257 Non-local Network 2017.11 / 240 CBAM 2018.07 / 54 GCNet 2019.04 / - ResNetv2 2016.03 / 1926 Inception-2,3 2015.12 / 3752 Inception-4 Inception-ResNet 2016.02 / 2152 NASNet 2017.07. / 417 𝑇𝑜𝑑𝑎𝑦
Plain Networks PR-163: CNN_Attention_Networks AlexNet 2012 / 39646 VGG 2014.09 / 22554 ResNet 2015.12 / 21871 • PlainNetworksusingMax-Pooling • LowPerformance/LargeParameters,Operations ILSVRC12 ILSVRC14
ResNet PR-163: CNN_Attention_Networks AlexNet 2012 / 39646 VGG 2014.09 / 22554 ResNet 2015.12 / 21871 • DeeperNetworksusingSkip-Connection Res Block Res Block Res Block Res Block ILSVRC15
ResNet ResBlock PR-163: CNN_Attention_Networks Res Block Res Block Res Block Res Block 1x1 3x3 1x1 c c/4 c/4 c BatchNorm ReLU xN
ResNet Variants PR-163: CNN_Attention_Networks AlexNet 2012 / 39646 VGG 2014.09 / 22554 ResNet 2015.12 / 21871 WideResNet 2016.05 / 1063 ResNetv2 2016.03 / 1926 • Pre-activationResNet 1x1 3x3 1x1 c c/4 c/4 c
ResNet Variants PR-163: CNN_Attention_Networks AlexNet 2012 / 39646 VGG 2014.09 / 22554 ResNet 2015.12 / 21871 WideResNet 2016.05 / 1063 ResNetv2 2016.03 / 1926 • WiderChannelResNet 3x3 3x3 c c x k c
ResNet with Cardinality PR-163: CNN_Attention_Networks AlexNet 2012 / 39646 VGG 2014.09 / 22554 ResNet 2015.12 / 21871 ResNeXt 2016.11 / 913 Xception 2016.10 / 847 MobileNet 2017.04 / 1553 ShuffleNet 2017.07 / 407 • Cardinality =GroupConv • PR-034:Xception • PR-044:MobileNet • PR-054:SuffleNet/ResNeXt • ModifyConvolutionOperators
ResNeXt (C=32) PR-163: CNN_Attention_Networks 256 256 1x1 1x1 1x1 3x3 3x3 3x3 1x1 1x1 1x1 4 4 256 + 32 Blocks ...... ...... ......
PR-163: CNN_Attention_Networks 256 256 3x3 3x3 3x3 128 4 4 1x1 1x1 C O N C A T G R O U P C O N V 128 ResNeXt (C=32) with Group Conv.
PR-163: CNN_Attention_Networks 256 256 1x1 1x1 1x1 3x3 3x3 3x3 1x1 1x1 1x1 4 4 256 + 32 Blocks X (1x1x4)x32 Summation ...... ...... ...... ResNeXt (C=32)
PR-163: CNN_Attention_Networks 256 256 3x3 3x3 3x3 128 4 4 1x1 1x1 C O N C A T G R O U P C O N V 128 ... ...X 1x1x128Summation ...... ...... ResNeXt (C=32) with Group Conv.
Xception (G=Channel) PR-163: CNN_Attention_Networks 256 256 3x3 3x3 3x3 128 1 1 1x1 1x1 C O N C A T G R O U P C O N V 128 ...... ......
MobileNet / ShuffleNet PR-163: CNN_Attention_Networks • MobileNet:LightweightXception • Xception: 1x1→3x3Depthwise • MobileNet: 3x3Depthwise→1x1 • ShuffleNet:LightweightResNeXt+ChannelShuffle
DenseNet PR-163: CNN_Attention_Networks AlexNet 2012 / 39646 VGG 2014.09 / 22554 ResNet 2015.12 / 21871 DenseNet 2016.08 / 3591 • DenseNet:ConcatPreviousLayers:PR-028 Dense Block Dense Block Dense Block Dense Block
DenseBlock PR-163: CNN_Attention_Networks Dense Block Dense Block Dense Block Dense Block 1x1 3x3 Conv Conv Conv ConvConv
Inception / NASNet PR-163: CNN_Attention_Networks AlexNet 2012 / 39646 VGG 2014.09 / 22554 GoogleNet 2014.09 / 13233 ResNet 2015.12 / 21871 Inception-2,3 2015.12 / 3752 Inception-4 Inception-ResNet 2016.02 / 2152 NASNet 2017.07. / 417 • EngineeredNetworks • PR-034:Inception • PR-069:NASNet ILSVRC14
CNN Performances PR-163: CNN_Attention_Networks https://github.com/CeLuigi/models-comparison.pytorch
CNN Review PR-163: CNN_Attention_Networks Category Networks Pros Cons Plain AlexNet, VGG Simple Good Transfer Low Performance ResNet ResNet Simple Cardinality ResNeXt/Xception MobileNet/ShuffleNet Cost Efficient + Performance Group Conv DenseNet DenseNet Cost Efficient + Performance Memory I/O Engineering Inception NASNet SoTA Complex
CNN Review PR-163: CNN_Attention_Networks Category Networks Pros Cons Plain AlexNet, VGG Simple Good Transfer Low Performance ResNet ResNet Simple Cardinality ResNeXt/Xception MobileNet/ShuffleNet Cost Efficient + Performance Group Conv DenseNet DenseNet Cost Efficient + Performance Memory I/O Engineering Inception NASNet SoTA Complex Attention Module SENet, CBAM, GCNet Simple + Performance
CNN Review PR-163: CNN_Attention_Networks
CNN x Attention
CNN Attention-Networks PR-163: CNN_Attention_Networks AlexNet 2012 / 39646 VGG 2014.09 / 22554 GoogleNet 2014.09 / 13233 ResNet 2015.12 / 21871 DenseNet 2016.08 / 3591 ResNeXt 2016.11 / 913 Xception 2016.10 / 847 MobileNet 2017.04 / 1553 ShuffleNet 2017.07 / 407 WideResNet 2016.05 / 1063 SE-Net 2017.09 / 724 Residual Attention Net 2017.04 / 257 Non-local Network 2017.11 / 240 CBAM 2018.07 / 54 GCNet 2019.04 / - ResNetv2 2016.03 / 1926 Inception-2,3 2015.12 / 3752 Inception-4 Inception-ResNet 2016.02 / 2152 NASNet 2017.07. / 417 ILSVRC17
Spatial Transformer Networks (NIPS15, PR-011) PR-163: CNN_Attention_Networks • Recalibration(withTransform)
Residual Attention Network (CVPR17) PR-163: CNN_Attention_Networks
Residual Attention Network (CVPR17) PR-163: CNN_Attention_Networks c + Conv(1x1) Conv(3x3) Conv(1x1) • OriginalResNet(BottleNeckBlock)
Residual Attention Network (CVPR17) PR-163: CNN_Attention_Networks c + Conv(1x1) Conv(3x3) Conv(1x1) Mask Generation Networks Sigmoid ReLU x • Recalibrate{Spatial+Channel) • ActuallyNotResidual
Residual Attention Network (CVPR17) PR-163: CNN_Attention_Networks • Results:InterpretableFeatures
Squeeze-and-Excitation Networks (CVPR18) PR-163: CNN_Attention_Networks c c + x c cc/16 1x1 1x1 AvgPool Conv(1x1) Conv(3x3) Conv(1x1) Sigmoid ReLU BN • RecalibrateChannelOnly
Squeeze-and-Excitation Networks (CVPR18) PR-163: CNN_Attention_Networks c c + x c cc/16 1x1 1x1 AvgPool Conv(1x1) Conv(3x3) Conv(1x1) Sigmoid ReLU BN • RecalibrateChannelOnly Per-Channel GlobalSpatialContext (Squeeze)
Squeeze-and-Excitation Networks (CVPR18) PR-163: CNN_Attention_Networks c c + x c cc/16 1x1 1x1 AvgPool Conv(1x1) Conv(3x3) Conv(1x1) Sigmoid ReLU BN • RecalibrateChannelOnly ModelingInter-Channel Relationship(Excitation)
Squeeze-and-Excitation Networks (CVPR18) PR-163: CNN_Attention_Networks
Squeeze-and-Excitation Networks (CVPR18) PR-163: CNN_Attention_Networks
Squeeze-and-Excitation Networks (CVPR18) PR-163: CNN_Attention_Networks • ChannelRecalibrationStats.
Bottleneck Attention Networks (BMVC18) PR-163: CNN_Attention_Networks c + Conv(1x1) Conv(3x3) Conv(1x1) 1x1 1x1 AvgPool + x + Sigmoid ReLU BN 3x3 Dilated Conv 1x1 r r 3x3 Channel Attention Spatial Attention
Convolutional Block Attention Networks (ECCV18) PR-163: CNN_Attention_Networks c + x c cc/16 AvgPool MaxPool x 7x7 Channel-wise AvgPool MaxPool c Conv(1x1) Conv(3x3) Conv(1x1) + Share 1x1 Sigmoid ReLU BN Channel Attention Spatial Attention
BAM / CBAM Results PR-163: CNN_Attention_Networks
RAN / SE / BAM / CBAM Comparison PR-163: CNN_Attention_Networks Network Module Position Attention RAN (CVPR17) Modified ChannelxSpatial 3D SE (CVPR18) In the ResBlock Channel BAM (BMVC18) Before the Stride=2 ResBlock Channel, Spatial Parallel CBAM (ECCV18) In the ResBlock Channel, Spatial Sequential
Non-local Networks PR-163: CNN_Attention_Networks • RepresentSpatial-Only Soft maxHxWxC HxWxC HWxC HWxC CxHW HWxHW HWxC Quary Key Value HxWxC +
Global-Context Attention Networks PR-163: CNN_Attention_Networks Softmax HxWxC HxWxC HWxC 1xHW C Key Value HxWxC + +
Global-Context Attention Networks PR-163: CNN_Attention_Networks Softmax HxWxC HxWxC HWxC 1xHW C Key Value HxWxC + + SpatialRepresentation
Global-Context Attention Networks PR-163: CNN_Attention_Networks Softmax HxWxC HxWxC HWxC 1xHW C Key Value HxWxC + + SpatialRepresentation ChannelAttention
Global-Context Attention Networks PR-163: CNN_Attention_Networks QuaryIndependentRepresentation→Recalibration
Global-Context Attention Networks PR-163: CNN_Attention_Networks NLNet SimplifiedNLNet SENet GCNet SpatialWeightedSum PerPixel (HxW) SpatialWeightedSum Shared (Scalar) SpatialAggregation (GlobalAvgPool) →forChannelRecalibration SpatialWeightedSum →forChannelRecalibration
Global-Context Attention Networks PR-163: CNN_Attention_Networks QuaryIndependentRepresentation→Recalibration Non-localNetworksMeetSqueeze-ExcitationNetworksandBeyond(GCNet)
Global-Context Attention Networks PR-163: CNN_Attention_Networks
Summary PR-163: CNN_Attention_Networks Network Attention Spatial Modeling RAN (CVPR17) ChannelxSpatial 3D Network SE (CVPR18) Channel Avg Pool BAM (BMVC18) Channel, Spatial Parallel Avg Pool CBAM (ECCV18) Channel, Spatial Sequential Avg Pool + Max Pool NLNet (CVPR18) Spatial (Representation) Non-local Representation GCNet (Preprint19) Channel Non-local Representation
CNN x Attention: Other Vision Tasks
Self-Attention GAN PR-163: CNN_Attention_Networks
Style Transfer (CVPR19) PR-163: CNN_Attention_Networks ArbitraryStyleTransferwithStyle-AttentionalNetworks
PSANet (ECCV18) / Context Encoding (CVPR18) / OCNet (2018) PR-163: CNN_Attention_Networks
Dual Attention Network (CVPR19) PR-163: CNN_Attention_Networks
Criss-Cross Non-local Attention Networks (2019) PR-163: CNN_Attention_Networks
Semantic Segmentation PR-163: CNN_Attention_Networks Network Performance (Cityscape) mIoU Structure DenseASPP (CVPR18) 80.6 DenseNet PSANet (ECCV18) 80.1 Spatial Attention Context Encoding (CVPR18) - Channel Attention CCNet (Arxiv19) 81.4 Fast NL-Net DANet (CVPR19) 81.5 NL-Net (Spatial + Channel) OCNet (Arxiv18) 81.7 NL-Net + PSP
Non-local in SISR PR-163: CNN_Attention_Networks
Single Image Super-Resolution PR-163: CNN_Attention_Networks Network Performance (set5, PSNR) Structure RDN (CVPR18) 38.24 / 32.47 (x2, x4) DenseNet RNRN (ICLR19) 38.17 / 32.49 NL-Net RCAN (ECCV18) 38.27 / 32.63 Channel Attention SAN (CVPR19) 38.31 / 32.64 Channel Attention + NL-Net
Conclusion PR-163: CNN_Attention_Networks • Attention (Recurrent) vs Self-Attention (Feed-Forward) • Representation vs Recalibration • Channel Attention: Simple • Spatial Attention: Global Information
ThankYou Q&A?

Recommandé

Convolutional Neural Networks
Convolutional Neural NetworksConvolutional Neural Networks
Convolutional Neural NetworksAshray Bhandare
 
Transfer Learning
Transfer LearningTransfer Learning
Transfer LearningHichem Felouat
 
Machine Learning - Object Detection and Classification
Machine Learning - Object Detection and ClassificationMachine Learning - Object Detection and Classification
Machine Learning - Object Detection and ClassificationVikas Jain
 
Notes on attention mechanism
Notes on attention mechanismNotes on attention mechanism
Notes on attention mechanismKhang Pham
 
CNN Tutorial
CNN TutorialCNN Tutorial
CNN TutorialSungjoon Choi
 
Deep Learning - Convolutional Neural Networks
Deep Learning - Convolutional Neural NetworksDeep Learning - Convolutional Neural Networks
Deep Learning - Convolutional Neural NetworksChristian Perone
 
Action Recognition (Thesis presentation)
Action Recognition (Thesis presentation)Action Recognition (Thesis presentation)
Action Recognition (Thesis presentation)nikhilus85
 
Convolutional Neural Network and Its Applications
Convolutional Neural Network and Its ApplicationsConvolutional Neural Network and Its Applications
Convolutional Neural Network and Its ApplicationsKasun Chinthaka Piyarathna
 

Recommandé

Convolutional Neural Networks
Convolutional Neural NetworksConvolutional Neural Networks
Convolutional Neural NetworksAshray Bhandare
 
Transfer Learning
Transfer LearningTransfer Learning
Transfer LearningHichem Felouat
 
Machine Learning - Object Detection and Classification
Machine Learning - Object Detection and ClassificationMachine Learning - Object Detection and Classification
Machine Learning - Object Detection and ClassificationVikas Jain
 
Notes on attention mechanism
Notes on attention mechanismNotes on attention mechanism
Notes on attention mechanismKhang Pham
 
CNN Tutorial
CNN TutorialCNN Tutorial
CNN TutorialSungjoon Choi
 
Deep Learning - Convolutional Neural Networks
Deep Learning - Convolutional Neural NetworksDeep Learning - Convolutional Neural Networks
Deep Learning - Convolutional Neural NetworksChristian Perone
 
Action Recognition (Thesis presentation)
Action Recognition (Thesis presentation)Action Recognition (Thesis presentation)
Action Recognition (Thesis presentation)nikhilus85
 
Convolutional Neural Network and Its Applications
Convolutional Neural Network and Its ApplicationsConvolutional Neural Network and Its Applications
Convolutional Neural Network and Its ApplicationsKasun Chinthaka Piyarathna
 
Machine Learning With Logistic Regression
Machine Learning With Logistic RegressionMachine Learning With Logistic Regression
Machine Learning With Logistic RegressionKnoldus Inc.
 
PhD Defense
PhD DefensePhD Defense
PhD DefenseTaehoon Lee
 
Deep Learning - CNN and RNN
Deep Learning - CNN and RNNDeep Learning - CNN and RNN
Deep Learning - CNN and RNNAshray Bhandare
 
Convolutional neural network
Convolutional neural network Convolutional neural network
Convolutional neural network Yan Xu
 
Pr045 deep lab_semantic_segmentation
Pr045 deep lab_semantic_segmentationPr045 deep lab_semantic_segmentation
Pr045 deep lab_semantic_segmentationTaeoh Kim
 
Convolutional Neural Network Models - Deep Learning
Convolutional Neural Network Models - Deep LearningConvolutional Neural Network Models - Deep Learning
Convolutional Neural Network Models - Deep LearningMohamed Loey
 
Semantic segmentation with Convolutional Neural Network Approaches
Semantic segmentation with Convolutional Neural Network ApproachesSemantic segmentation with Convolutional Neural Network Approaches
Semantic segmentation with Convolutional Neural Network ApproachesFellowship at Vodafone FutureLab
 
Optimization for Deep Learning
Optimization for Deep LearningOptimization for Deep Learning
Optimization for Deep LearningSebastian Ruder
 
Attention Models (D3L6 2017 UPC Deep Learning for Computer Vision)
Attention Models (D3L6 2017 UPC Deep Learning for Computer Vision)Attention Models (D3L6 2017 UPC Deep Learning for Computer Vision)
Attention Models (D3L6 2017 UPC Deep Learning for Computer Vision)Universitat Politècnica de Catalunya
 
Convolutional Neural Networks (CNN)
Convolutional Neural Networks (CNN)Convolutional Neural Networks (CNN)
Convolutional Neural Networks (CNN)Gaurav Mittal
 
1D Convolutional Neural Networks for Time Series Modeling - Nathan Janos, Jef...
1D Convolutional Neural Networks for Time Series Modeling - Nathan Janos, Jef...1D Convolutional Neural Networks for Time Series Modeling - Nathan Janos, Jef...
1D Convolutional Neural Networks for Time Series Modeling - Nathan Janos, Jef...PyData
 
Introduction to object detection
Introduction to object detectionIntroduction to object detection
Introduction to object detectionBrodmann17
 
Introduction For seq2seq(sequence to sequence) and RNN
Introduction For seq2seq(sequence to sequence) and RNNIntroduction For seq2seq(sequence to sequence) and RNN
Introduction For seq2seq(sequence to sequence) and RNNHye-min Ahn
 
Attention in Deep Learning
Attention in Deep LearningAttention in Deep Learning
Attention in Deep Learning健程 杨
 
Diffusion models beat gans on image synthesis
Diffusion models beat gans on image synthesisDiffusion models beat gans on image synthesis
Diffusion models beat gans on image synthesisBeerenSahu
 
Transfer Learning and Fine-tuning Deep Neural Networks
Transfer Learning and Fine-tuning Deep Neural Networks Transfer Learning and Fine-tuning Deep Neural Networks
Transfer Learning and Fine-tuning Deep Neural NetworksPyData
 
Convolutional neural network from VGG to DenseNet
Convolutional neural network from VGG to DenseNetConvolutional neural network from VGG to DenseNet
Convolutional neural network from VGG to DenseNetSungminYou
 
Attention is All You Need (Transformer)
Attention is All You Need (Transformer)Attention is All You Need (Transformer)
Attention is All You Need (Transformer)Jeong-Gwan Lee
 
Attention mechanism 소개 자료
Attention mechanism 소개 자료Attention mechanism 소개 자료
Attention mechanism 소개 자료Whi Kwon
 
Rnn & Lstm
Rnn & LstmRnn & Lstm
Rnn & LstmSubash Chandra Pakhrin
 
One shot learning - deep learning ( meta learn )
One shot learning - deep learning ( meta learn )One shot learning - deep learning ( meta learn )
One shot learning - deep learning ( meta learn )Dong Heon Cho
 
Hardware Acceleration for Machine Learning
Hardware Acceleration for Machine LearningHardware Acceleration for Machine Learning
Hardware Acceleration for Machine LearningCastLabKAIST
 

Contenu connexe

Tendances

Machine Learning With Logistic Regression
Machine Learning With Logistic RegressionMachine Learning With Logistic Regression
Machine Learning With Logistic RegressionKnoldus Inc.
 
Deep Learning - CNN and RNN
Deep Learning - CNN and RNNDeep Learning - CNN and RNN
Deep Learning - CNN and RNNAshray Bhandare
 
Convolutional neural network
Convolutional neural network Convolutional neural network
Convolutional neural network Yan Xu
 
Pr045 deep lab_semantic_segmentation
Pr045 deep lab_semantic_segmentationPr045 deep lab_semantic_segmentation
Pr045 deep lab_semantic_segmentationTaeoh Kim
 
Convolutional Neural Network Models - Deep Learning
Convolutional Neural Network Models - Deep LearningConvolutional Neural Network Models - Deep Learning
Convolutional Neural Network Models - Deep LearningMohamed Loey
 
Semantic segmentation with Convolutional Neural Network Approaches
Semantic segmentation with Convolutional Neural Network ApproachesSemantic segmentation with Convolutional Neural Network Approaches
Semantic segmentation with Convolutional Neural Network ApproachesFellowship at Vodafone FutureLab
 
Optimization for Deep Learning
Optimization for Deep LearningOptimization for Deep Learning
Optimization for Deep LearningSebastian Ruder
 
Convolutional Neural Networks (CNN)
Convolutional Neural Networks (CNN)Convolutional Neural Networks (CNN)
Convolutional Neural Networks (CNN)Gaurav Mittal
 
1D Convolutional Neural Networks for Time Series Modeling - Nathan Janos, Jef...
1D Convolutional Neural Networks for Time Series Modeling - Nathan Janos, Jef...1D Convolutional Neural Networks for Time Series Modeling - Nathan Janos, Jef...
1D Convolutional Neural Networks for Time Series Modeling - Nathan Janos, Jef...PyData
 
Introduction to object detection
Introduction to object detectionIntroduction to object detection
Introduction to object detectionBrodmann17
 
Introduction For seq2seq(sequence to sequence) and RNN
Introduction For seq2seq(sequence to sequence) and RNNIntroduction For seq2seq(sequence to sequence) and RNN
Introduction For seq2seq(sequence to sequence) and RNNHye-min Ahn
 
Attention in Deep Learning
Attention in Deep LearningAttention in Deep Learning
Attention in Deep Learning健程 杨
 
Diffusion models beat gans on image synthesis
Diffusion models beat gans on image synthesisDiffusion models beat gans on image synthesis
Diffusion models beat gans on image synthesisBeerenSahu
 
Transfer Learning and Fine-tuning Deep Neural Networks
Transfer Learning and Fine-tuning Deep Neural Networks Transfer Learning and Fine-tuning Deep Neural Networks
Transfer Learning and Fine-tuning Deep Neural NetworksPyData
 
Convolutional neural network from VGG to DenseNet
Convolutional neural network from VGG to DenseNetConvolutional neural network from VGG to DenseNet
Convolutional neural network from VGG to DenseNetSungminYou
 
Attention is All You Need (Transformer)
Attention is All You Need (Transformer)Attention is All You Need (Transformer)
Attention is All You Need (Transformer)Jeong-Gwan Lee
 
Attention mechanism 소개 자료
Attention mechanism 소개 자료Attention mechanism 소개 자료
Attention mechanism 소개 자료Whi Kwon
 

Tendances (20)

Machine Learning With Logistic Regression
Machine Learning With Logistic RegressionMachine Learning With Logistic Regression
Machine Learning With Logistic Regression
 
PhD Defense
PhD DefensePhD Defense
PhD Defense
 
Deep Learning - CNN and RNN
Deep Learning - CNN and RNNDeep Learning - CNN and RNN
Deep Learning - CNN and RNN
 
Convolutional neural network
Convolutional neural network Convolutional neural network
Convolutional neural network
 
Pr045 deep lab_semantic_segmentation
Pr045 deep lab_semantic_segmentationPr045 deep lab_semantic_segmentation
Pr045 deep lab_semantic_segmentation
 
Convolutional Neural Network Models - Deep Learning
Convolutional Neural Network Models - Deep LearningConvolutional Neural Network Models - Deep Learning
Convolutional Neural Network Models - Deep Learning
 
Semantic segmentation with Convolutional Neural Network Approaches
Semantic segmentation with Convolutional Neural Network ApproachesSemantic segmentation with Convolutional Neural Network Approaches
Semantic segmentation with Convolutional Neural Network Approaches
 
Optimization for Deep Learning
Optimization for Deep LearningOptimization for Deep Learning
Optimization for Deep Learning
 
Attention Models (D3L6 2017 UPC Deep Learning for Computer Vision)
Attention Models (D3L6 2017 UPC Deep Learning for Computer Vision)Attention Models (D3L6 2017 UPC Deep Learning for Computer Vision)
Attention Models (D3L6 2017 UPC Deep Learning for Computer Vision)
 
Convolutional Neural Networks (CNN)
Convolutional Neural Networks (CNN)Convolutional Neural Networks (CNN)
Convolutional Neural Networks (CNN)
 
1D Convolutional Neural Networks for Time Series Modeling - Nathan Janos, Jef...
1D Convolutional Neural Networks for Time Series Modeling - Nathan Janos, Jef...1D Convolutional Neural Networks for Time Series Modeling - Nathan Janos, Jef...
1D Convolutional Neural Networks for Time Series Modeling - Nathan Janos, Jef...
 
Introduction to object detection
Introduction to object detectionIntroduction to object detection
Introduction to object detection
 
Introduction For seq2seq(sequence to sequence) and RNN
Introduction For seq2seq(sequence to sequence) and RNNIntroduction For seq2seq(sequence to sequence) and RNN
Introduction For seq2seq(sequence to sequence) and RNN
 
Attention in Deep Learning
Attention in Deep LearningAttention in Deep Learning
Attention in Deep Learning
 
Diffusion models beat gans on image synthesis
Diffusion models beat gans on image synthesisDiffusion models beat gans on image synthesis
Diffusion models beat gans on image synthesis
 
Transfer Learning and Fine-tuning Deep Neural Networks
Transfer Learning and Fine-tuning Deep Neural Networks Transfer Learning and Fine-tuning Deep Neural Networks
Transfer Learning and Fine-tuning Deep Neural Networks
 
Convolutional neural network from VGG to DenseNet
Convolutional neural network from VGG to DenseNetConvolutional neural network from VGG to DenseNet
Convolutional neural network from VGG to DenseNet
 
Attention is All You Need (Transformer)
Attention is All You Need (Transformer)Attention is All You Need (Transformer)
Attention is All You Need (Transformer)
 
Attention mechanism 소개 자료
Attention mechanism 소개 자료Attention mechanism 소개 자료
Attention mechanism 소개 자료
 
Rnn & Lstm
Rnn & LstmRnn & Lstm
Rnn & Lstm
 

Similaire à CNN Attention Networks

One shot learning - deep learning ( meta learn )
One shot learning - deep learning ( meta learn )One shot learning - deep learning ( meta learn )
One shot learning - deep learning ( meta learn )Dong Heon Cho
 
Hardware Acceleration for Machine Learning
Hardware Acceleration for Machine LearningHardware Acceleration for Machine Learning
Hardware Acceleration for Machine LearningCastLabKAIST
 
Neural network basic and introduction of Deep learning
Neural network basic and introduction of Deep learningNeural network basic and introduction of Deep learning
Neural network basic and introduction of Deep learningTapas Majumdar
 
Predicting churn in telco industry: machine learning approach - Marko Mitić
Predicting churn in telco industry: machine learning approach - Marko Mitić Predicting churn in telco industry: machine learning approach - Marko Mitić
Predicting churn in telco industry: machine learning approach - Marko MitićInstitute of Contemporary Sciences
 
Tutorial on Object Detection (Faster R-CNN)
Tutorial on Object Detection (Faster R-CNN)Tutorial on Object Detection (Faster R-CNN)
Tutorial on Object Detection (Faster R-CNN)Hwa Pyung Kim
 
SPICE-MATEX @ DAC15
SPICE-MATEX @ DAC15SPICE-MATEX @ DAC15
SPICE-MATEX @ DAC15Hao Zhuang
 
Parallel Computing 2007: Bring your own parallel application
Parallel Computing 2007: Bring your own parallel applicationParallel Computing 2007: Bring your own parallel application
Parallel Computing 2007: Bring your own parallel applicationGeoffrey Fox
 
Clustering: Large Databases in data mining
Clustering: Large Databases in data miningClustering: Large Databases in data mining
Clustering: Large Databases in data miningZHAO Sam
 
JAISTサマースクール2016「脳を知るための理論」講義04 Neural Networks and Neuroscience
JAISTサマースクール2016「脳を知るための理論」講義04 Neural Networks and Neuroscience JAISTサマースクール2016「脳を知るための理論」講義04 Neural Networks and Neuroscience
JAISTサマースクール2016「脳を知るための理論」講義04 Neural Networks and Neuroscience hirokazutanaka
 
A CGRA-based Approach for Accelerating Convolutional Neural Networks
A CGRA-based Approach for Accelerating Convolutional Neural NetworksA CGRA-based Approach for Accelerating Convolutional Neural Networks
A CGRA-based Approach for Accelerating Convolutional Neural NetworksShinya Takamaeda-Y
 
ENBIS 2018 presentation on Deep k-Means
ENBIS 2018 presentation on Deep k-MeansENBIS 2018 presentation on Deep k-Means
ENBIS 2018 presentation on Deep k-Meanstthonet
 
convolutional_neural_networks in deep learning
convolutional_neural_networks in deep learningconvolutional_neural_networks in deep learning
convolutional_neural_networks in deep learningssusere5ddd6
 
Deep learning study 2
Deep learning study 2Deep learning study 2
Deep learning study 2San Kim
 
pptx - Psuedo Random Generator for Halfspaces
pptx - Psuedo Random Generator for Halfspacespptx - Psuedo Random Generator for Halfspaces
pptx - Psuedo Random Generator for Halfspacesbutest
 
pptx - Psuedo Random Generator for Halfspaces
pptx - Psuedo Random Generator for Halfspacespptx - Psuedo Random Generator for Halfspaces
pptx - Psuedo Random Generator for Halfspacesbutest
 
Lecture 7: Recurrent Neural Networks
Lecture 7: Recurrent Neural NetworksLecture 7: Recurrent Neural Networks
Lecture 7: Recurrent Neural NetworksSang Jun Lee
 
SCOR: Constraint Programming-based Northbound Interface for SDN
SCOR: Constraint Programming-based Northbound Interface for SDNSCOR: Constraint Programming-based Northbound Interface for SDN
SCOR: Constraint Programming-based Northbound Interface for SDNFarzaneh Pakzad
 
Log Analytics in Datacenter with Apache Spark and Machine Learning
Log Analytics in Datacenter with Apache Spark and Machine LearningLog Analytics in Datacenter with Apache Spark and Machine Learning
Log Analytics in Datacenter with Apache Spark and Machine LearningPiotr Tylenda
 
Log Analytics in Datacenter with Apache Spark and Machine Learning
Log Analytics in Datacenter with Apache Spark and Machine LearningLog Analytics in Datacenter with Apache Spark and Machine Learning
Log Analytics in Datacenter with Apache Spark and Machine LearningAgnieszka Potulska
 

Similaire à CNN Attention Networks (20)

One shot learning - deep learning ( meta learn )
One shot learning - deep learning ( meta learn )One shot learning - deep learning ( meta learn )
One shot learning - deep learning ( meta learn )
 
Hardware Acceleration for Machine Learning
Hardware Acceleration for Machine LearningHardware Acceleration for Machine Learning
Hardware Acceleration for Machine Learning
 
Neural network basic and introduction of Deep learning
Neural network basic and introduction of Deep learningNeural network basic and introduction of Deep learning
Neural network basic and introduction of Deep learning
 
Predicting churn in telco industry: machine learning approach - Marko Mitić
Predicting churn in telco industry: machine learning approach - Marko Mitić Predicting churn in telco industry: machine learning approach - Marko Mitić
Predicting churn in telco industry: machine learning approach - Marko Mitić
 
Tutorial on Object Detection (Faster R-CNN)
Tutorial on Object Detection (Faster R-CNN)Tutorial on Object Detection (Faster R-CNN)
Tutorial on Object Detection (Faster R-CNN)
 
SPICE-MATEX @ DAC15
SPICE-MATEX @ DAC15SPICE-MATEX @ DAC15
SPICE-MATEX @ DAC15
 
Parallel Computing 2007: Bring your own parallel application
Parallel Computing 2007: Bring your own parallel applicationParallel Computing 2007: Bring your own parallel application
Parallel Computing 2007: Bring your own parallel application
 
Clustering: Large Databases in data mining
Clustering: Large Databases in data miningClustering: Large Databases in data mining
Clustering: Large Databases in data mining
 
Ns2 by khan
Ns2 by khan Ns2 by khan
Ns2 by khan
 
JAISTサマースクール2016「脳を知るための理論」講義04 Neural Networks and Neuroscience
JAISTサマースクール2016「脳を知るための理論」講義04 Neural Networks and Neuroscience JAISTサマースクール2016「脳を知るための理論」講義04 Neural Networks and Neuroscience
JAISTサマースクール2016「脳を知るための理論」講義04 Neural Networks and Neuroscience
 
A CGRA-based Approach for Accelerating Convolutional Neural Networks
A CGRA-based Approach for Accelerating Convolutional Neural NetworksA CGRA-based Approach for Accelerating Convolutional Neural Networks
A CGRA-based Approach for Accelerating Convolutional Neural Networks
 
ENBIS 2018 presentation on Deep k-Means
ENBIS 2018 presentation on Deep k-MeansENBIS 2018 presentation on Deep k-Means
ENBIS 2018 presentation on Deep k-Means
 
convolutional_neural_networks in deep learning
convolutional_neural_networks in deep learningconvolutional_neural_networks in deep learning
convolutional_neural_networks in deep learning
 
Deep learning study 2
Deep learning study 2Deep learning study 2
Deep learning study 2
 
pptx - Psuedo Random Generator for Halfspaces
pptx - Psuedo Random Generator for Halfspacespptx - Psuedo Random Generator for Halfspaces
pptx - Psuedo Random Generator for Halfspaces
 
pptx - Psuedo Random Generator for Halfspaces
pptx - Psuedo Random Generator for Halfspacespptx - Psuedo Random Generator for Halfspaces
pptx - Psuedo Random Generator for Halfspaces
 
Lecture 7: Recurrent Neural Networks
Lecture 7: Recurrent Neural NetworksLecture 7: Recurrent Neural Networks
Lecture 7: Recurrent Neural Networks
 
SCOR: Constraint Programming-based Northbound Interface for SDN
SCOR: Constraint Programming-based Northbound Interface for SDNSCOR: Constraint Programming-based Northbound Interface for SDN
SCOR: Constraint Programming-based Northbound Interface for SDN
 
Log Analytics in Datacenter with Apache Spark and Machine Learning
Log Analytics in Datacenter with Apache Spark and Machine LearningLog Analytics in Datacenter with Apache Spark and Machine Learning
Log Analytics in Datacenter with Apache Spark and Machine Learning
 
Log Analytics in Datacenter with Apache Spark and Machine Learning
Log Analytics in Datacenter with Apache Spark and Machine LearningLog Analytics in Datacenter with Apache Spark and Machine Learning
Log Analytics in Datacenter with Apache Spark and Machine Learning
 

Plus de Taeoh Kim

PR 127: FaceNet
PR 127: FaceNetPR 127: FaceNet
PR 127: FaceNetTaeoh Kim
 
PR 113: The Perception Distortion Tradeoff
PR 113: The Perception Distortion TradeoffPR 113: The Perception Distortion Tradeoff
PR 113: The Perception Distortion TradeoffTaeoh Kim
 
PR 103: t-SNE
PR 103: t-SNEPR 103: t-SNE
PR 103: t-SNETaeoh Kim
 
Pr083 Non-local Neural Networks
Pr083 Non-local Neural NetworksPr083 Non-local Neural Networks
Pr083 Non-local Neural NetworksTaeoh Kim
 
Pr072 deep compression
Pr072 deep compressionPr072 deep compression
Pr072 deep compressionTaeoh Kim
 
Pr057 mask rcnn
Pr057 mask rcnnPr057 mask rcnn
Pr057 mask rcnnTaeoh Kim
 

Plus de Taeoh Kim (6)

PR 127: FaceNet
PR 127: FaceNetPR 127: FaceNet
PR 127: FaceNet
 
PR 113: The Perception Distortion Tradeoff
PR 113: The Perception Distortion TradeoffPR 113: The Perception Distortion Tradeoff
PR 113: The Perception Distortion Tradeoff
 
PR 103: t-SNE
PR 103: t-SNEPR 103: t-SNE
PR 103: t-SNE
 
Pr083 Non-local Neural Networks
Pr083 Non-local Neural NetworksPr083 Non-local Neural Networks
Pr083 Non-local Neural Networks
 
Pr072 deep compression
Pr072 deep compressionPr072 deep compression
Pr072 deep compression
 
Pr057 mask rcnn
Pr057 mask rcnnPr057 mask rcnn
Pr057 mask rcnn
 

Dernier

Double Revolving field theory-how the rotor develops torque
Double Revolving field theory-how the rotor develops torqueDouble Revolving field theory-how the rotor develops torque
Double Revolving field theory-how the rotor develops torqueBhangaleSonal
 
Thermal Engineering -unit - III & IV.ppt
Thermal Engineering -unit - III & IV.pptThermal Engineering -unit - III & IV.ppt
Thermal Engineering -unit - III & IV.pptDineshKumar4165
 
Unit 4_Part 1 CSE2001 Exception Handling and Function Template and Class Temp...
Unit 4_Part 1 CSE2001 Exception Handling and Function Template and Class Temp...Unit 4_Part 1 CSE2001 Exception Handling and Function Template and Class Temp...
Unit 4_Part 1 CSE2001 Exception Handling and Function Template and Class Temp...drmkjayanthikannan
 
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXssuser89054b
 
Thermal Engineering-R & A / C - unit - V
Thermal Engineering-R & A / C - unit - VThermal Engineering-R & A / C - unit - V
Thermal Engineering-R & A / C - unit - VDineshKumar4165
 
A CASE STUDY ON CERAMIC INDUSTRY OF BANGLADESH.pptx
A CASE STUDY ON CERAMIC INDUSTRY OF BANGLADESH.pptxA CASE STUDY ON CERAMIC INDUSTRY OF BANGLADESH.pptx
A CASE STUDY ON CERAMIC INDUSTRY OF BANGLADESH.pptxmaisarahman1
 
A Study of Urban Area Plan for Pabna Municipality
A Study of Urban Area Plan for Pabna MunicipalityA Study of Urban Area Plan for Pabna Municipality
A Study of Urban Area Plan for Pabna MunicipalityMorshed Ahmed Rahath
 
Orlando’s Arnold Palmer Hospital Layout Strategy-1.pptx
Orlando’s Arnold Palmer Hospital Layout Strategy-1.pptxOrlando’s Arnold Palmer Hospital Layout Strategy-1.pptx
Orlando’s Arnold Palmer Hospital Layout Strategy-1.pptxMuhammadAsimMuhammad6
 
Moment Distribution Method For Btech Civil
Moment Distribution Method For Btech CivilMoment Distribution Method For Btech Civil
Moment Distribution Method For Btech CivilVinayVitekari
 
HAND TOOLS USED AT ELECTRONICS WORK PRESENTED BY KOUSTAV SARKAR
HAND TOOLS USED AT ELECTRONICS WORK PRESENTED BY KOUSTAV SARKARHAND TOOLS USED AT ELECTRONICS WORK PRESENTED BY KOUSTAV SARKAR
HAND TOOLS USED AT ELECTRONICS WORK PRESENTED BY KOUSTAV SARKARKOUSTAV SARKAR
 
Jaipur ❤CALL GIRL 0000000000❤CALL GIRLS IN Jaipur ESCORT SERVICE❤CALL GIRL IN...
Jaipur ❤CALL GIRL 0000000000❤CALL GIRLS IN Jaipur ESCORT SERVICE❤CALL GIRL IN...Jaipur ❤CALL GIRL 0000000000❤CALL GIRLS IN Jaipur ESCORT SERVICE❤CALL GIRL IN...
Jaipur ❤CALL GIRL 0000000000❤CALL GIRLS IN Jaipur ESCORT SERVICE❤CALL GIRL IN...jabtakhaidam7
 
"Lesotho Leaps Forward: A Chronicle of Transformative Developments"
"Lesotho Leaps Forward: A Chronicle of Transformative Developments""Lesotho Leaps Forward: A Chronicle of Transformative Developments"
"Lesotho Leaps Forward: A Chronicle of Transformative Developments"mphochane1998
 
Learn the concepts of Thermodynamics on Magic Marks
Learn the concepts of Thermodynamics on Magic MarksLearn the concepts of Thermodynamics on Magic Marks
Learn the concepts of Thermodynamics on Magic MarksMagic Marks
 
Online electricity billing project report..pdf
Online electricity billing project report..pdfOnline electricity billing project report..pdf
Online electricity billing project report..pdfKamal Acharya
 
Introduction to Serverless with AWS Lambda
Introduction to Serverless with AWS LambdaIntroduction to Serverless with AWS Lambda
Introduction to Serverless with AWS LambdaOmar Fathy
 
Hospital management system project report.pdf
Hospital management system project report.pdfHospital management system project report.pdf
Hospital management system project report.pdfKamal Acharya
 
Design For Accessibility: Getting it right from the start
Design For Accessibility: Getting it right from the startDesign For Accessibility: Getting it right from the start
Design For Accessibility: Getting it right from the startQuintin Balsdon
 
Unleashing the Power of the SORA AI lastest leap
Unleashing the Power of the SORA AI lastest leapUnleashing the Power of the SORA AI lastest leap
Unleashing the Power of the SORA AI lastest leapRishantSharmaFr
 

Dernier (20)

Double Revolving field theory-how the rotor develops torque
Double Revolving field theory-how the rotor develops torqueDouble Revolving field theory-how the rotor develops torque
Double Revolving field theory-how the rotor develops torque
 
Thermal Engineering -unit - III & IV.ppt
Thermal Engineering -unit - III & IV.pptThermal Engineering -unit - III & IV.ppt
Thermal Engineering -unit - III & IV.ppt
 
Unit 4_Part 1 CSE2001 Exception Handling and Function Template and Class Temp...
Unit 4_Part 1 CSE2001 Exception Handling and Function Template and Class Temp...Unit 4_Part 1 CSE2001 Exception Handling and Function Template and Class Temp...
Unit 4_Part 1 CSE2001 Exception Handling and Function Template and Class Temp...
 
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
 
Thermal Engineering-R & A / C - unit - V
Thermal Engineering-R & A / C - unit - VThermal Engineering-R & A / C - unit - V
Thermal Engineering-R & A / C - unit - V
 
A CASE STUDY ON CERAMIC INDUSTRY OF BANGLADESH.pptx
A CASE STUDY ON CERAMIC INDUSTRY OF BANGLADESH.pptxA CASE STUDY ON CERAMIC INDUSTRY OF BANGLADESH.pptx
A CASE STUDY ON CERAMIC INDUSTRY OF BANGLADESH.pptx
 
Integrated Test Rig For HTFE-25 - Neometrix
Integrated Test Rig For HTFE-25 - NeometrixIntegrated Test Rig For HTFE-25 - Neometrix
Integrated Test Rig For HTFE-25 - Neometrix
 
A Study of Urban Area Plan for Pabna Municipality
A Study of Urban Area Plan for Pabna MunicipalityA Study of Urban Area Plan for Pabna Municipality
A Study of Urban Area Plan for Pabna Municipality
 
Orlando’s Arnold Palmer Hospital Layout Strategy-1.pptx
Orlando’s Arnold Palmer Hospital Layout Strategy-1.pptxOrlando’s Arnold Palmer Hospital Layout Strategy-1.pptx
Orlando’s Arnold Palmer Hospital Layout Strategy-1.pptx
 
Moment Distribution Method For Btech Civil
Moment Distribution Method For Btech CivilMoment Distribution Method For Btech Civil
Moment Distribution Method For Btech Civil
 
HAND TOOLS USED AT ELECTRONICS WORK PRESENTED BY KOUSTAV SARKAR
HAND TOOLS USED AT ELECTRONICS WORK PRESENTED BY KOUSTAV SARKARHAND TOOLS USED AT ELECTRONICS WORK PRESENTED BY KOUSTAV SARKAR
HAND TOOLS USED AT ELECTRONICS WORK PRESENTED BY KOUSTAV SARKAR
 
Jaipur ❤CALL GIRL 0000000000❤CALL GIRLS IN Jaipur ESCORT SERVICE❤CALL GIRL IN...
Jaipur ❤CALL GIRL 0000000000❤CALL GIRLS IN Jaipur ESCORT SERVICE❤CALL GIRL IN...Jaipur ❤CALL GIRL 0000000000❤CALL GIRLS IN Jaipur ESCORT SERVICE❤CALL GIRL IN...
Jaipur ❤CALL GIRL 0000000000❤CALL GIRLS IN Jaipur ESCORT SERVICE❤CALL GIRL IN...
 
"Lesotho Leaps Forward: A Chronicle of Transformative Developments"
"Lesotho Leaps Forward: A Chronicle of Transformative Developments""Lesotho Leaps Forward: A Chronicle of Transformative Developments"
"Lesotho Leaps Forward: A Chronicle of Transformative Developments"
 
Learn the concepts of Thermodynamics on Magic Marks
Learn the concepts of Thermodynamics on Magic MarksLearn the concepts of Thermodynamics on Magic Marks
Learn the concepts of Thermodynamics on Magic Marks
 
Call Girls in South Ex (delhi) call me [🔝9953056974🔝] escort service 24X7
Call Girls in South Ex (delhi) call me [🔝9953056974🔝] escort service 24X7Call Girls in South Ex (delhi) call me [🔝9953056974🔝] escort service 24X7
Call Girls in South Ex (delhi) call me [🔝9953056974🔝] escort service 24X7
 
Online electricity billing project report..pdf
Online electricity billing project report..pdfOnline electricity billing project report..pdf
Online electricity billing project report..pdf
 
Introduction to Serverless with AWS Lambda
Introduction to Serverless with AWS LambdaIntroduction to Serverless with AWS Lambda
Introduction to Serverless with AWS Lambda
 
Hospital management system project report.pdf
Hospital management system project report.pdfHospital management system project report.pdf
Hospital management system project report.pdf
 
Design For Accessibility: Getting it right from the start
Design For Accessibility: Getting it right from the startDesign For Accessibility: Getting it right from the start
Design For Accessibility: Getting it right from the start
 
Unleashing the Power of the SORA AI lastest leap
Unleashing the Power of the SORA AI lastest leapUnleashing the Power of the SORA AI lastest leap
Unleashing the Power of the SORA AI lastest leap
 

CNN Attention Networks