Visual concept learning
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Deep Learning is really good when dealing with images where conventional machine learning methodologies fell short. Still when training a deep neural network we need a lot of labeled examples unlike a human which can learn an object from even a single image. Collecting labeled images is not only cumbersome but also expensive. Training a classifier with few examples will simply overfit on the training dataset and will not generalise well. All in all in this talk I will cover some of the approaches that can be used to train a Neural network based image classifier when given few examples from different classes. Audience will get to learn the concept of few shot learning, current research trends, common approaches to tackle this problem.
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Visual concept learning
- 1. Visual Concept Learning Vaibhav Singh Machine Learning Engineer https://www.linkedin.com/in/techytux/
- 2. Hello! I am Vaibhav You can contact me on linkedin https://www.linkedin.com/in/techytux/ ● Payment Fraud @ Klarna ● Previously worked in Moderation Team @ OLX Group ● Software Engineer turned Machine Learning Engineer
- 3. AGENDAPopular datasets Paper summaries What is few shot learning Approaches and related work
- 4. What the heck is this talk about Zero / few shot learning 1
- 5. ““Learning how to represent objects (e.g. images) given only a few instances of each object! ”
- 9. Lamprey
- 10. Star nosed mole
- 12. ???
- 13. Classify a reference image into one of N candidate classes, given K representatives of each class. For example, 2-shot 4-way classification: Concept : K Shot N Way
- 14. Humans can learn new concepts fast without much training data But for Deep Learning Networks we need hundreds or even thousands of examples What did we learn
- 15. Motivation for few shot learning ● Machine learning fails to generalize when little supervised information is available. ● Getting labeled data can be expensive. ● Adding domain information / features for each task is cumbersome and does not scale well. ● Gradient based methods like Adagrad, ADAM, Adadelta, Nesterov momentum weren’t designed to perform well under the constraint of a set number of examples ● Random initialization of weights hurts the networks ability to converge with few updates
- 16. What are commonly used datasets for few shot learning Popular Datasets 2
- 17. Created by Lake in 2015 105 x 105 Grayscale Images 1623 chars from 50 alphabets each with 20 handwritten examples Omniglot
- 18. Mini Imagenet 100 real world classes, each with 600 examples
- 20. Approaches to do low shot learning Data Based ● Siamese networks (Koch et al. 2015) ● Memory Augmented Networks (Santoro et al. 2016) ● Matching networks (Vinyals et al. 2016) ● Meta Optimization (Ravi & Larochelle 2017) Metric or Parameter Based ● Tap into external data sources ● Generate new labels using semi supervised learning. Propagate labels using a distance metric ● Generate new examples using GAN’s. For example: Create new images of birds from different angles if there are examples present in the training set
- 21. Related Work ● Fei Fei li -- Bayesian framework -- previously learned classes can be leveraged to help forecast future ones. Fei Fei et al, 2003; Fei Fei et al, 2006 ● Lake -- HBPL -- Concept learning through probabilistic programming ○ Penstrokes as domain information Lake et al. 2015
- 23. Siamese Neural Networks for One Shot Image Recognition Koch et al. 2015
- 24. Verification Networks Koch, Zemel, Salak., 2015
- 25. Koch, Zemel, Salak., 2015 Network Architecture
- 26. Evaluation Koch, Zemel, Salak., 2015
- 27. Concept : Episodes 1 2 3 1 4
- 28. Concept : Meta Learning
- 29. Concept : Neural Turing Machine Graves et al. 2014
- 30. One shot learning with Memory Augmented Networks Santoro et al. 2016
- 31. Memory Augmented Networks Santoro et al. 2016
- 32. Evaluation
- 33. Matching Networks for One Shot Learning Vinyals et al. 2017
- 34. Matching Networks ● Matching nets is built on top of ○ metric learning on Deep Neural features and ○ memory networks, basically external memory with attention mechanism used to access the memory ● Proposes that training and test conditions must match
- 35. Matching Networks Vinyals et al. 2016 ● Network is shown sample set S and asked to give probability that x(hat) is an instance of class in set S.
- 36. Evaluation Vinyals et al. 2016
- 37. Evaluation Vinyals et al. 2016
- 38. Optimization as a model for few shot learning Ravi & Larochelle 2017
- 39. ● Instead of using gradient based optimization they propose ○ LSTM based meta learner to learn exact optimization algorithm used to train another neural network classifier ● General initialization of network so that it converges faster Meta Optimization
- 40. Network structure Rave & Larochelle 2017
- 41. Evaluation Rave & Larochelle 2017
- 42. References ● One shot Imitation Learning -- Duan et al 2017 ● Low-shot Visual Recognition by Shrinking and Hallucinating Features -- Hariharan, Girshick 2017 ● NTM -- https://github.com/tristandeleu/ntm-one-shot/ ● Siamese Networks -- https://sorenbouma.github.io/blog/oneshot/