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Introduction to Few shot learning

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Ridge-i, Inc.
Ridge-i, Inc.

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

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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

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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