SlideShare a Scribd company logo

Introduction to Few shot learning

•
•
Ridge-i, Inc.
Ridge-i, Inc.

This is a presentation by our Engineer Noel Tay about Few Shot Learning.

Technology
1 of 22
Download to read offline
Study Meeting Presentation:
 
 Really Quick Intro on Few-Shot Learning
 Author: Noel Tay
 
 Date: 2020/08/28 

Getting things in perspective … Predictor Train Dog Let us limit the discussion to: - Image classification - Supervised learning - Close set recognition - Requires a huge number of data for each task - New task needs to be retrained - However, humans can learn it effortlessly Image source from “The CIFAR-10 dataset” (https://www.cs.toronto.edu/~kriz/cifar.html) Image Source: https://unsplash.com/ Image source from “The CIFAR-10 dataset” (https://www.cs.toronto.edu/~kriz/cifar.html) Image Source: https://unsplash.com/
Getting things in perspective … How many people are there? What is this place? Where is this place? What is the time of day? What is the temperature? What is the mood? Do they practice social distancing? Do they wear masks? Humans: - Can decompose/manipulate representations - Accommodate to task - Don’t need extra training Data Bias! 3 Image Source: https://unsplash.com/
Getting things in perspective … To mimic human ability: - Finding good priors Blank slate vs. innate behaviors - Good representations Learning with the help of ‘unlabeled’ data, such as self-supervised learning - Transfer learning Knowledge transfer from one task to the other (For example, improving face recognition with another model that deals with different expressions) - Few-shot learning This is what we will be talking about! 4
Few-Shot Learning - To classify new data after being given a few samples - Extreme case is called one-shot learning Class 1 Class 2 - It is not to solve insufficient data issue, but to provide an alternative way to handle little data per class 5 ? Source: https://unsplash.com/
Urban Rural Truck Car Few-Shot Learning - To quickly switch to new classification task with few samples 6 Image source: https://unsplash.com/
Yellow Red Few-Shot Learning - To quickly switch to new classification task with few samples 7 Image source: https://unsplash.com/
Query set Support set Few-Shot Learning - N-way-K-shot Truck Car 2-way-4-shot ? 8 Task Image source: https://unsplash.com/
Few-Shot Learning Meta-Learning Framework - Conventional approach is to train the model using dataset to perform classification - Meta-learning is to ‘train’ the model to learn how to use dataset to perform classification (Learning to Learn) Class 1 Class 2 ? Class 1 Class 2 ? 9
Meta-Learning predictor Dog Train Testing 10 Conventional Image source from: https://lilianweng.github.io/lil-log/2018/11/30/meta-learning.html
Meta-Learning: Learning to Learn Testing predictor Train Dog There are no sample-class binding 11 Each data sample is a Task Image source from: https://lilianweng.github.io/lil-log/2018/11/30/meta-learning.html Image source from: https://lilianweng.github.io/lil-log/2018/11/30/meta-learning.html
12 Class 1 Class 2 Class 1 Class 2 Class 1 Class 2 Meta-Learning: Classes, samples and labels shuffling Image source modified from: https://lilianweng.github.io/lil-log/2018/11/30/meta-learning.html
Meta-Learning - Based on similarity - Matching networks - Prototypical networks - Relation networks - Based on learning algorithm - Model agnostic meta-learning (MAML) - Memory augmented neural network - Based on data - Bayesian programs 13
Meta-Learning: Based on Similarity 0.08 0.02 0.1 0.8 x x x x sum Matching Network Prototypical Network 14 [1] Vinyals, O., Blundell, C., Lillicrap, T., & Wierstra, D. (2016). Matching networks for one shot learning. Advances in neural information processing systems, 29, 3630-3638. [2] Snell, J., Swersky, K., & Zemel, R. S. (2017). Prototypical networks for few-shot learning. arXiv preprint arXiv:1703.05175. Image source from original paper [1] Image source from original paper [2] Image modified from original paper [1]
Meta-Learning: Based on Similarity 15 Image source from: https://www.borealisai.com/en/blog/tutorial-2-few-shot-learning-and-meta-learning-i/
Meta-Learning: Based on Learning Algorithm Memory Augmented Neural Network (MANN) Learns the algorithm to store and retrieve memories [1] 16 dog cat dog dog cat … NULL [1] Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., & Lillicrap, T. (2016, June). Meta-learning with memory-augmented neural networks. In International conference on machine learning (pp. 1842-1850). PMLR. Image source from original paper [1] Image source from original paper [1] Image source: https://unsplash.com/
w1 w2 Meta-Learning: Based on Learning Algorithm Model agnostic meta-learning (MAML) [1] 17 Task 1 Task 2 Task 3 Init [1] Finn, C., Abbeel, P., & Levine, S. (2017, July). Model-agnostic meta-learning for fast adaptation of deep networks. In International Conference on Machine Learning (pp. 1126-1135). PMLR. Image source modified from: https://lilianweng.github.io/lil-log/2018/11/30/meta-learnin g.html Image source modified from: https://lilianweng.github.io/lil-log/2018/11/30/meta-learnin g.html
w1 w2 Meta-Learning: Based on Learning Algorithm Model agnostic meta-learning (MAML) 18 Task 1 Task 2 Task 3 data for task1 learning data for task2 learning data for task3 learning
w1 w2 Meta-Learning: Based on Learning Algorithm Model agnostic meta-learning (MAML) 19 Task 1 Task 2 Task 3 data for task1 meta learning data for task2 meta learning data for task3 meta learning
w1 w2 Meta-Learning: Based on Learning Algorithm Model agnostic meta-learning (MAML) 20 Task 1 Task 2 Task 3
Meta-Learning: Based on Data Modeling through Bayesian Programs 21 … - Structure of the model contains information on how the output is created (prior) - Meta-learning learns a way for various Bayesian program modules to combine to express unseen data - Remember probabilistic programming with Pyro? [1] Lake, B. M., Salakhutdinov, R., & Tenenbaum, J. B. (2015). Human-level concept learning through probabilistic program induction. Science, 350(6266), 1332-1338. Image source from original paper [1]
22 Consideration (after getting things in perspective…) - Do I need ‘learning to learn’ or just lack of data Does my application justify its usage - Is my dataset sufficient enough Huge amount of data doesn’t mean sufficient - What prior knowledge I have For example: data model, invariance assumption - Any training constraints I can impose For example: curriculum learning, multi-loss, feature space constraints

Recommended

Few shot learning/ one shot learning/ machine learning
Few shot learning/ one shot learning/ machine learningFew shot learning/ one shot learning/ machine learning
Few shot learning/ one shot learning/ machine learning
ﺁﺻﻒ ﻋﻠﯽ ﻣﯿﺮ
 
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Model-Agnostic Meta-Learning for Fast Adaptation of Deep NetworksModel-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Yoonho Lee
 
Zero shot-learning: paper presentation
Zero shot-learning: paper presentationZero shot-learning: paper presentation
Zero shot-learning: paper presentation
Jérémie Kalfon
 
Deep learning - A Visual Introduction
Deep learning - A Visual IntroductionDeep learning - A Visual Introduction
Deep learning - A Visual Introduction
Lukas Masuch
 
Recurrent Neural Networks. Part 1: Theory
Recurrent Neural Networks. Part 1: TheoryRecurrent Neural Networks. Part 1: Theory
Recurrent Neural Networks. Part 1: Theory
Andrii Gakhov
 
Convolutional Neural Networks
Convolutional Neural NetworksConvolutional Neural Networks
Convolutional Neural Networks
Ashray Bhandare
 
Siamese networks
Siamese networksSiamese networks
Siamese networks
Nicholas McClure
 
Long Short Term Memory
Long Short Term MemoryLong Short Term Memory
Long Short Term Memory
Yan Xu
 

Recommended

Few shot learning/ one shot learning/ machine learning
Few shot learning/ one shot learning/ machine learningFew shot learning/ one shot learning/ machine learning
Few shot learning/ one shot learning/ machine learning
ﺁﺻﻒ ﻋﻠﯽ ﻣﯿﺮ
 
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Model-Agnostic Meta-Learning for Fast Adaptation of Deep NetworksModel-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Yoonho Lee
 
Zero shot-learning: paper presentation
Zero shot-learning: paper presentationZero shot-learning: paper presentation
Zero shot-learning: paper presentation
Jérémie Kalfon
 
Deep learning - A Visual Introduction
Deep learning - A Visual IntroductionDeep learning - A Visual Introduction
Deep learning - A Visual Introduction
Lukas Masuch
 
Recurrent Neural Networks. Part 1: Theory
Recurrent Neural Networks. Part 1: TheoryRecurrent Neural Networks. Part 1: Theory
Recurrent Neural Networks. Part 1: Theory
Andrii Gakhov
 
Convolutional Neural Networks
Convolutional Neural NetworksConvolutional Neural Networks
Convolutional Neural Networks
Ashray Bhandare
 
Siamese networks
Siamese networksSiamese networks
Siamese networks
Nicholas McClure
 
Long Short Term Memory
Long Short Term MemoryLong Short Term Memory
Long Short Term Memory
Yan Xu
 
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
Hye-min Ahn
 
Convolutional Neural Network (CNN) - image recognition
Convolutional Neural Network (CNN) - image recognitionConvolutional Neural Network (CNN) - image recognition
Convolutional Neural Network (CNN) - image recognition
YUNG-KUEI CHEN
 
Generative adversarial networks
Generative adversarial networksGenerative adversarial networks
Generative adversarial networks
남주 김
 
Relational knowledge distillation
Relational knowledge distillationRelational knowledge distillation
Relational knowledge distillation
NAVER Engineering
 
Introduction to continual learning
Introduction to continual learningIntroduction to continual learning
Introduction to continual learning
Nguyen Giang
 
Introduction to CNN
Introduction to CNNIntroduction to CNN
Introduction to CNN
Shuai Zhang
 
Introduction to Deep learning
Introduction to Deep learningIntroduction to Deep learning
Introduction to Deep learning
Massimiliano Ruocco
 
Optimization as a model for few shot learning
Optimization as a model for few shot learningOptimization as a model for few shot learning
Optimization as a model for few shot learning
Katy Lee
 
Convolutional neural network
Convolutional neural network Convolutional neural network
Convolutional neural network
Yan Xu
 
Deep Learning - Convolutional Neural Networks
Deep Learning - Convolutional Neural NetworksDeep Learning - Convolutional Neural Networks
Deep Learning - Convolutional Neural Networks
Christian Perone
 
Optimization in Deep Learning
Optimization in Deep LearningOptimization in Deep Learning
Optimization in Deep Learning
Yan Xu
 
Optimization for Deep Learning
Optimization for Deep LearningOptimization for Deep Learning
Optimization for Deep Learning
Sebastian Ruder
 
Meta-Learning Presentation
Meta-Learning PresentationMeta-Learning Presentation
Meta-Learning Presentation
AkshayaNagarajan10
 
Convolutional Neural Network Models - Deep Learning
Convolutional Neural Network Models - Deep LearningConvolutional Neural Network Models - Deep Learning
Convolutional Neural Network Models - Deep Learning
Mohamed Loey
 
Deep Dive into Hyperparameter Tuning
Deep Dive into Hyperparameter TuningDeep Dive into Hyperparameter Tuning
Deep Dive into Hyperparameter Tuning
Shubhmay Potdar
 
Sequence Modelling with Deep Learning
Sequence Modelling with Deep LearningSequence Modelling with Deep Learning
Sequence Modelling with Deep Learning
Natasha Latysheva
 
Convolutional Neural Network and Its Applications
Convolutional Neural Network and Its ApplicationsConvolutional Neural Network and Its Applications
Convolutional Neural Network and Its Applications
Kasun Chinthaka Piyarathna
 
One shot learning
One shot learningOne shot learning
One shot learning
Vuong Ho Ngoc
 
AlexNet(ImageNet Classification with Deep Convolutional Neural Networks)
AlexNet(ImageNet Classification with Deep Convolutional Neural Networks)AlexNet(ImageNet Classification with Deep Convolutional Neural Networks)
AlexNet(ImageNet Classification with Deep Convolutional Neural Networks)
Fellowship at Vodafone FutureLab
 
Introduction to Deep learning
Introduction to Deep learningIntroduction to Deep learning
Introduction to Deep learning
leopauly
 
Logistic Regression In Data Science
Logistic Regression In Data ScienceLogistic Regression In Data Science
Logistic Regression In Data Science
Edureka!
 
Monet banksy may2014
Monet banksy may2014Monet banksy may2014
Monet banksy may2014
GailStrachan
 

More Related Content

What's hot

Relational knowledge distillation
Relational knowledge distillationRelational knowledge distillation
Relational knowledge distillation
NAVER Engineering
 
Sequence Modelling with Deep Learning
Sequence Modelling with Deep LearningSequence Modelling with Deep Learning
Sequence Modelling with Deep Learning
Natasha Latysheva
 
AlexNet(ImageNet Classification with Deep Convolutional Neural Networks)
AlexNet(ImageNet Classification with Deep Convolutional Neural Networks)AlexNet(ImageNet Classification with Deep Convolutional Neural Networks)
AlexNet(ImageNet Classification with Deep Convolutional Neural Networks)
Fellowship at Vodafone FutureLab
 

What's hot (20)

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
 
Convolutional Neural Network (CNN) - image recognition
Convolutional Neural Network (CNN) - image recognitionConvolutional Neural Network (CNN) - image recognition
Convolutional Neural Network (CNN) - image recognition
 
Generative adversarial networks
Generative adversarial networksGenerative adversarial networks
Generative adversarial networks
 
Relational knowledge distillation
Relational knowledge distillationRelational knowledge distillation
Relational knowledge distillation
 
Introduction to continual learning
Introduction to continual learningIntroduction to continual learning
Introduction to continual learning
 
Introduction to CNN
Introduction to CNNIntroduction to CNN
Introduction to CNN
 
Introduction to Deep learning
Introduction to Deep learningIntroduction to Deep learning
Introduction to Deep learning
 
Optimization as a model for few shot learning
Optimization as a model for few shot learningOptimization as a model for few shot learning
Optimization as a model for few shot learning
 
Convolutional neural network
Convolutional neural network Convolutional neural network
Convolutional neural network
 
Deep Learning - Convolutional Neural Networks
Deep Learning - Convolutional Neural NetworksDeep Learning - Convolutional Neural Networks
Deep Learning - Convolutional Neural Networks
 
Optimization in Deep Learning
Optimization in Deep LearningOptimization in Deep Learning
Optimization in Deep Learning
 
Optimization for Deep Learning
Optimization for Deep LearningOptimization for Deep Learning
Optimization for Deep Learning
 
Meta-Learning Presentation
Meta-Learning PresentationMeta-Learning Presentation
Meta-Learning Presentation
 
Convolutional Neural Network Models - Deep Learning
Convolutional Neural Network Models - Deep LearningConvolutional Neural Network Models - Deep Learning
Convolutional Neural Network Models - Deep Learning
 
Deep Dive into Hyperparameter Tuning
Deep Dive into Hyperparameter TuningDeep Dive into Hyperparameter Tuning
Deep Dive into Hyperparameter Tuning
 
Sequence Modelling with Deep Learning
Sequence Modelling with Deep LearningSequence Modelling with Deep Learning
Sequence Modelling with Deep Learning
 
Convolutional Neural Network and Its Applications
Convolutional Neural Network and Its ApplicationsConvolutional Neural Network and Its Applications
Convolutional Neural Network and Its Applications
 
One shot learning
One shot learningOne shot learning
One shot learning
 
AlexNet(ImageNet Classification with Deep Convolutional Neural Networks)
AlexNet(ImageNet Classification with Deep Convolutional Neural Networks)AlexNet(ImageNet Classification with Deep Convolutional Neural Networks)
AlexNet(ImageNet Classification with Deep Convolutional Neural Networks)
 
Introduction to Deep learning
Introduction to Deep learningIntroduction to Deep learning
Introduction to Deep learning
 

Similar to Introduction to Few shot learning

Monet banksy may2014
Monet banksy may2014Monet banksy may2014
Monet banksy may2014
GailStrachan
 
Developing Computational Skills in the Sciences with Matlab Webinar 2017
Developing Computational Skills in the Sciences with Matlab Webinar 2017Developing Computational Skills in the Sciences with Matlab Webinar 2017
Developing Computational Skills in the Sciences with Matlab Webinar 2017
SERC at Carleton College
 
dark futures_v3
dark futures_v3dark futures_v3
dark futures_v3
Stella Lee
 
Deep Learning Explained
Deep Learning ExplainedDeep Learning Explained
Deep Learning Explained
Melanie Swan
 
Data scientist enablement dse 400 week 3 roadmap
Data scientist enablement dse 400 week 3 roadmapData scientist enablement dse 400 week 3 roadmap
Data scientist enablement dse 400 week 3 roadmap
Dr. Mohan K. Bavirisetty
 
Philosophy of Deep Learning
Philosophy of Deep LearningPhilosophy of Deep Learning
Philosophy of Deep Learning
Melanie Swan
 
Helping More Teachers Discover Your Digital Resources
Helping More Teachers Discover Your Digital ResourcesHelping More Teachers Discover Your Digital Resources
Helping More Teachers Discover Your Digital Resources
Darren Milligan
 
Developments in Adaptive E-Learning
Developments in Adaptive E-LearningDevelopments in Adaptive E-Learning
Developments in Adaptive E-Learning
Christian Glahn
 

Similar to Introduction to Few shot learning (20)

Logistic Regression In Data Science
Logistic Regression In Data ScienceLogistic Regression In Data Science
Logistic Regression In Data Science
 
Monet banksy may2014
Monet banksy may2014Monet banksy may2014
Monet banksy may2014
 
Developing Computational Skills in the Sciences with Matlab Webinar 2017
Developing Computational Skills in the Sciences with Matlab Webinar 2017Developing Computational Skills in the Sciences with Matlab Webinar 2017
Developing Computational Skills in the Sciences with Matlab Webinar 2017
 
dark futures_v3
dark futures_v3dark futures_v3
dark futures_v3
 
Automatic Learning Image Objects via Incremental Model
Automatic Learning Image Objects via Incremental ModelAutomatic Learning Image Objects via Incremental Model
Automatic Learning Image Objects via Incremental Model
 
‘Responsible’ learning analytics: A tentative proposal
‘Responsible’ learning analytics: A tentative proposal‘Responsible’ learning analytics: A tentative proposal
‘Responsible’ learning analytics: A tentative proposal
 
An invitation to a conversation: Towards a South African ethical use of stude...
An invitation to a conversation: Towards a South African ethical use of stude...An invitation to a conversation: Towards a South African ethical use of stude...
An invitation to a conversation: Towards a South African ethical use of stude...
 
How can we train with few data
How can we train with few dataHow can we train with few data
How can we train with few data
 
Sentiment Analysis In Retail Domain
Sentiment Analysis In Retail DomainSentiment Analysis In Retail Domain
Sentiment Analysis In Retail Domain
 
Deep Learning Explained
Deep Learning ExplainedDeep Learning Explained
Deep Learning Explained
 
Fast AI with Image Classification.pptx
Fast AI with Image Classification.pptxFast AI with Image Classification.pptx
Fast AI with Image Classification.pptx
 
A Blind Date With (Big) Data: Student Data in (Higher) Education
A Blind Date With (Big) Data: Student Data in (Higher) EducationA Blind Date With (Big) Data: Student Data in (Higher) Education
A Blind Date With (Big) Data: Student Data in (Higher) Education
 
Intro to LLMs
Intro to LLMsIntro to LLMs
Intro to LLMs
 
Data scientist enablement dse 400 week 3 roadmap
Data scientist enablement dse 400 week 3 roadmapData scientist enablement dse 400 week 3 roadmap
Data scientist enablement dse 400 week 3 roadmap
 
Analysing image collections with the computer vision network approach
Analysing image collections with the computer vision network approachAnalysing image collections with the computer vision network approach
Analysing image collections with the computer vision network approach
 
Philosophy of Deep Learning
Philosophy of Deep LearningPhilosophy of Deep Learning
Philosophy of Deep Learning
 
Active learning
Active learningActive learning
Active learning
 
Helping More Teachers Discover Your Digital Resources
Helping More Teachers Discover Your Digital ResourcesHelping More Teachers Discover Your Digital Resources
Helping More Teachers Discover Your Digital Resources
 
Developments in Adaptive E-Learning
Developments in Adaptive E-LearningDevelopments in Adaptive E-Learning
Developments in Adaptive E-Learning
 
Deep Learning: Application Landscape - March 2018
Deep Learning: Application Landscape - March 2018Deep Learning: Application Landscape - March 2018
Deep Learning: Application Landscape - March 2018
 

More from Ridge-i, Inc.

More from Ridge-i, Inc. (8)

Unsupervised Video Anomaly Detection: A brief overview
Unsupervised Video Anomaly Detection: A brief overviewUnsupervised Video Anomaly Detection: A brief overview
Unsupervised Video Anomaly Detection: A brief overview
 
Continual Learning Introduction
Continual Learning IntroductionContinual Learning Introduction
Continual Learning Introduction
 
Explainable AI
Explainable AIExplainable AI
Explainable AI
 
May internship challenge: Font Generator
May internship challenge: Font GeneratorMay internship challenge: Font Generator
May internship challenge: Font Generator
 
How to learn with non-reliable labels?
How to learn with non-reliable labels?How to learn with non-reliable labels?
How to learn with non-reliable labels?
 
How to learn with non-reliable labels? (Japanese version)
How to learn with non-reliable labels? (Japanese version)How to learn with non-reliable labels? (Japanese version)
How to learn with non-reliable labels? (Japanese version)
 
May internship challenge: User Authentication System only using image data: C...
May internship challenge: User Authentication System only using image data: C...May internship challenge: User Authentication System only using image data: C...
May internship challenge: User Authentication System only using image data: C...
 
May internship challenge: Estimating Distance between Two Balls App
May internship challenge: Estimating Distance between Two Balls AppMay internship challenge: Estimating Distance between Two Balls App
May internship challenge: Estimating Distance between Two Balls App
 

Recently uploaded

Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot TakeoffStrategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
sammart93
 
A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?
Igalia
 
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc
 
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
?#DUbAI#??##{{(☎️+971_581248768%)**%*]'#abortion pills for sale in dubai@
 
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
panagenda
 
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc
 

Recently uploaded (20)

A Beginners Guide to Building a RAG App Using Open Source Milvus
A Beginners Guide to Building a RAG App Using Open Source MilvusA Beginners Guide to Building a RAG App Using Open Source Milvus
A Beginners Guide to Building a RAG App Using Open Source Milvus
 
Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)
 
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWEREMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot TakeoffStrategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
 
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of Terraform
 
A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
 
Navi Mumbai Call Girls 🥰 8617370543 Service Offer VIP Hot Model
Navi Mumbai Call Girls 🥰 8617370543 Service Offer VIP Hot ModelNavi Mumbai Call Girls 🥰 8617370543 Service Offer VIP Hot Model
Navi Mumbai Call Girls 🥰 8617370543 Service Offer VIP Hot Model
 
DBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor Presentation
 
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherStrategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a Fresher
 
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
 
Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...
Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...
Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...
 
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
 
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
 
Apidays Singapore 2024 - Modernizing Securities Finance by Madhu Subbu
Apidays Singapore 2024 - Modernizing Securities Finance by Madhu SubbuApidays Singapore 2024 - Modernizing Securities Finance by Madhu Subbu
Apidays Singapore 2024 - Modernizing Securities Finance by Madhu Subbu
 
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
 
AXA XL - Insurer Innovation Award Americas 2024
AXA XL - Insurer Innovation Award Americas 2024AXA XL - Insurer Innovation Award Americas 2024
AXA XL - Insurer Innovation Award Americas 2024
 
Artificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : UncertaintyArtificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : Uncertainty
 
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
 

Introduction to Few shot learning

  • 1. Study Meeting Presentation:
 
 Really Quick Intro on Few-Shot Learning
 Author: Noel Tay
 
 Date: 2020/08/28 

  • 2. Getting things in perspective … Predictor Train Dog Let us limit the discussion to: - Image classification - Supervised learning - Close set recognition - Requires a huge number of data for each task - New task needs to be retrained - However, humans can learn it effortlessly Image source from “The CIFAR-10 dataset” (https://www.cs.toronto.edu/~kriz/cifar.html) Image Source: https://unsplash.com/ Image source from “The CIFAR-10 dataset” (https://www.cs.toronto.edu/~kriz/cifar.html) Image Source: https://unsplash.com/
  • 3. Getting things in perspective … How many people are there? What is this place? Where is this place? What is the time of day? What is the temperature? What is the mood? Do they practice social distancing? Do they wear masks? Humans: - Can decompose/manipulate representations - Accommodate to task - Don’t need extra training Data Bias! 3 Image Source: https://unsplash.com/
  • 4. Getting things in perspective … To mimic human ability: - Finding good priors Blank slate vs. innate behaviors - Good representations Learning with the help of ‘unlabeled’ data, such as self-supervised learning - Transfer learning Knowledge transfer from one task to the other (For example, improving face recognition with another model that deals with different expressions) - Few-shot learning This is what we will be talking about! 4
  • 5. Few-Shot Learning - To classify new data after being given a few samples - Extreme case is called one-shot learning Class 1 Class 2 - It is not to solve insufficient data issue, but to provide an alternative way to handle little data per class 5 ? Source: https://unsplash.com/
  • 6. Urban Rural Truck Car Few-Shot Learning - To quickly switch to new classification task with few samples 6 Image source: https://unsplash.com/
  • 7. Yellow Red Few-Shot Learning - To quickly switch to new classification task with few samples 7 Image source: https://unsplash.com/
  • 8. Query set Support set Few-Shot Learning - N-way-K-shot Truck Car 2-way-4-shot ? 8 Task Image source: https://unsplash.com/
  • 9. Few-Shot Learning Meta-Learning Framework - Conventional approach is to train the model using dataset to perform classification - Meta-learning is to ‘train’ the model to learn how to use dataset to perform classification (Learning to Learn) Class 1 Class 2 ? Class 1 Class 2 ? 9
  • 10. Meta-Learning predictor Dog Train Testing 10 Conventional Image source from: https://lilianweng.github.io/lil-log/2018/11/30/meta-learning.html
  • 11. Meta-Learning: Learning to Learn Testing predictor Train Dog There are no sample-class binding 11 Each data sample is a Task Image source from: https://lilianweng.github.io/lil-log/2018/11/30/meta-learning.html Image source from: https://lilianweng.github.io/lil-log/2018/11/30/meta-learning.html
  • 12. 12 Class 1 Class 2 Class 1 Class 2 Class 1 Class 2 Meta-Learning: Classes, samples and labels shuffling Image source modified from: https://lilianweng.github.io/lil-log/2018/11/30/meta-learning.html
  • 13. Meta-Learning - Based on similarity - Matching networks - Prototypical networks - Relation networks - Based on learning algorithm - Model agnostic meta-learning (MAML) - Memory augmented neural network - Based on data - Bayesian programs 13
  • 14. Meta-Learning: Based on Similarity 0.08 0.02 0.1 0.8 x x x x sum Matching Network Prototypical Network 14 [1] Vinyals, O., Blundell, C., Lillicrap, T., & Wierstra, D. (2016). Matching networks for one shot learning. Advances in neural information processing systems, 29, 3630-3638. [2] Snell, J., Swersky, K., & Zemel, R. S. (2017). Prototypical networks for few-shot learning. arXiv preprint arXiv:1703.05175. Image source from original paper [1] Image source from original paper [2] Image modified from original paper [1]
  • 15. Meta-Learning: Based on Similarity 15 Image source from: https://www.borealisai.com/en/blog/tutorial-2-few-shot-learning-and-meta-learning-i/
  • 16. Meta-Learning: Based on Learning Algorithm Memory Augmented Neural Network (MANN) Learns the algorithm to store and retrieve memories [1] 16 dog cat dog dog cat … NULL [1] Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., & Lillicrap, T. (2016, June). Meta-learning with memory-augmented neural networks. In International conference on machine learning (pp. 1842-1850). PMLR. Image source from original paper [1] Image source from original paper [1] Image source: https://unsplash.com/
  • 17. w1 w2 Meta-Learning: Based on Learning Algorithm Model agnostic meta-learning (MAML) [1] 17 Task 1 Task 2 Task 3 Init [1] Finn, C., Abbeel, P., & Levine, S. (2017, July). Model-agnostic meta-learning for fast adaptation of deep networks. In International Conference on Machine Learning (pp. 1126-1135). PMLR. Image source modified from: https://lilianweng.github.io/lil-log/2018/11/30/meta-learnin g.html Image source modified from: https://lilianweng.github.io/lil-log/2018/11/30/meta-learnin g.html
  • 18. w1 w2 Meta-Learning: Based on Learning Algorithm Model agnostic meta-learning (MAML) 18 Task 1 Task 2 Task 3 data for task1 learning data for task2 learning data for task3 learning
  • 19. w1 w2 Meta-Learning: Based on Learning Algorithm Model agnostic meta-learning (MAML) 19 Task 1 Task 2 Task 3 data for task1 meta learning data for task2 meta learning data for task3 meta learning
  • 20. w1 w2 Meta-Learning: Based on Learning Algorithm Model agnostic meta-learning (MAML) 20 Task 1 Task 2 Task 3
  • 21. Meta-Learning: Based on Data Modeling through Bayesian Programs 21 … - Structure of the model contains information on how the output is created (prior) - Meta-learning learns a way for various Bayesian program modules to combine to express unseen data - Remember probabilistic programming with Pyro? [1] Lake, B. M., Salakhutdinov, R., & Tenenbaum, J. B. (2015). Human-level concept learning through probabilistic program induction. Science, 350(6266), 1332-1338. Image source from original paper [1]
  • 22. 22 Consideration (after getting things in perspective…) - Do I need ‘learning to learn’ or just lack of data Does my application justify its usage - Is my dataset sufficient enough Huge amount of data doesn’t mean sufficient - What prior knowledge I have For example: data model, invariance assumption - Any training constraints I can impose For example: curriculum learning, multi-loss, feature space constraints