GAN's (Generative Adversial Networks)
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Exposé sur les GAN's
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Recommandé
Recommandé
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.Loss Functions for Deep Learning - Javier Ruiz Hidalgo - UPC Barcelona 2018
Loss Functions for Deep Learning - Javier Ruiz Hidalgo - UPC Barcelona 2018Universitat Politècnica de Catalunya
We trained a large, deep convolutional neural network to classify the 1.2 million
high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 dif-
ferent classes. On the test data, we achieved top-1 and top-5 error rates of 37.5%
and 17.0% which is considerably better than the previous state-of-the-art. The
neural network, which has 60 million parameters and 650,000 neurons, consists
of five convolutional layers, some of which are followed by max-pooling layers,
and three fully-connected layers with a final 1000-way softmax. To make train-
ing faster, we used non-saturating neurons and a very efficient GPU implemen-
tation of the convolution operation. To reduce overfitting in the fully-connected
layers we employed a recently-developed regularization method called “dropout”
that proved to be very effective. We also entered a variant of this model in the
ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%,
compared to 26.2% achieved by the second-best entry.AlexNet(ImageNet Classification with Deep Convolutional Neural Networks)
AlexNet(ImageNet Classification with Deep Convolutional Neural Networks)Fellowship at Vodafone FutureLab
Contenu connexe
Tendances
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.Loss Functions for Deep Learning - Javier Ruiz Hidalgo - UPC Barcelona 2018
Loss Functions for Deep Learning - Javier Ruiz Hidalgo - UPC Barcelona 2018Universitat Politècnica de Catalunya
We trained a large, deep convolutional neural network to classify the 1.2 million
high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 dif-
ferent classes. On the test data, we achieved top-1 and top-5 error rates of 37.5%
and 17.0% which is considerably better than the previous state-of-the-art. The
neural network, which has 60 million parameters and 650,000 neurons, consists
of five convolutional layers, some of which are followed by max-pooling layers,
and three fully-connected layers with a final 1000-way softmax. To make train-
ing faster, we used non-saturating neurons and a very efficient GPU implemen-
tation of the convolution operation. To reduce overfitting in the fully-connected
layers we employed a recently-developed regularization method called “dropout”
that proved to be very effective. We also entered a variant of this model in the
ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%,
compared to 26.2% achieved by the second-best entry.AlexNet(ImageNet Classification with Deep Convolutional Neural Networks)
AlexNet(ImageNet Classification with Deep Convolutional Neural Networks)Fellowship at Vodafone FutureLab
Tendances (20)
Introduction to Generative Adversarial Networks (GANs)
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Generative Adversarial Networks (GANs) - Ian Goodfellow, OpenAI
Generative Adversarial Networks (GANs) - Ian Goodfellow, OpenAI
Machine Learning and Data Mining: 14 Evaluation and Credibility
Machine Learning and Data Mining: 14 Evaluation and Credibility
Loss Functions for Deep Learning - Javier Ruiz Hidalgo - UPC Barcelona 2018
Loss Functions for Deep Learning - Javier Ruiz Hidalgo - UPC Barcelona 2018
AlexNet(ImageNet Classification with Deep Convolutional Neural Networks)
AlexNet(ImageNet Classification with Deep Convolutional Neural Networks)
Deep Learning Tutorial | Deep Learning TensorFlow | Deep Learning With Neural...
Deep Learning Tutorial | Deep Learning TensorFlow | Deep Learning With Neural...
GAN's (Generative Adversial Networks)
- 1. Réseaux Antagonistes Génératifs • BELKACEMI Mohamed Seddik • LAKEHAL Walid
- 2. Plan 2 1. AI 2. GANs 3. Discriminateur 4. générateur 5. Principe de fonctionnement 6. Type des GANs 7. VAEs
- 4. 4 2. GANs
- 5. 5
- 6. ▰ Classificateur de données ▰ Joue le rôle de juge dans les GANs ▰ Souvent utilise les réseaux de neurones 6 3. Discriminateur
- 12. Buts des GANs 12
- 13. L’entrainement du Générateur et du Discriminateur 13
- 14. Cost function du discriminateur J(D)=- 1 𝑀𝑟𝑒𝑎𝑙 𝑖=1 𝑀𝑟𝑒𝑎𝑙 𝑌𝑟𝑒𝑎𝑙 ∗ 𝑙𝑜𝑔 𝐷 𝑥 − 1 𝑀𝑔𝑒𝑛 𝑖=1 𝑀𝑔𝑒𝑛 1 − 𝑌𝑔𝑒𝑛 ∗ 𝑙𝑜𝑔 1 − 𝐷 𝑔 𝑧 14
- 15. Cost function du générateur 15 J(G)=- 1 𝑀𝑔𝑒𝑛 𝑖=1 𝑀𝑔𝑒𝑛 𝑙𝑜𝑔 1 − 𝐷 𝑔 𝑧 • Cette équation signifie que G doit tromper D en minimisant ce que D essaye de minimiser
- 16. Types de GANs ▰ Deep convolutional GANs ▰ Conditional GAN 16
- 17. VAE 17
- 18. Remerciment 18