Paper review: "Model-Agnostic Meta-learning for fast adaptation of deep networks" by C. Finn et al (ICML2017) Presented at Tensorflow-KR paper review forum (#PR12) by Taesu Kim Paper link: https://arxiv.org/abs/1703.03400 Video link: https://youtu.be/fxJXXKZb-ik (in Korean) http://www.neosapience.com

1. Model-Agnostic Meta Learning
for fast adaptation of deep networks
Presented by Taesu Kim
June 24, 2018
C. Finn, P. Abbeel, S. Levine

2. Meta-learning: Learning to learn
Meta is a prefix used in English to indicate a concept which is an abstraction behind
another concept, used to complete or add to the latter.
-- From Wikipedia

3. Naïve approach to fine-tune
Is this the best way? Are you sure?

4. How to find good initial weights
a=f(x)

5. Model agnostic meta-learning algorithm

6. Few-shot supervised learning
For regression
For classification
K-shot, N-way classification
(2)
(3)

7. For reinforcement learning
Quickly acquire a policy for a new
task only a small amount of
experience in the test setting
(4)

8. Experimental evaluation
› To answer following questions
– Can MAML enable fast learning of new tasks?
– Can MAML be used for meta-learning in multiple different
domains, including supervised regression, classification, and
reinforcement learning?
– Can a model learned with MAML continue to improve with
additional gradient updates and/or examples?

9. Regression
› Sine wave fitting
– Amplitude: [0.1, 5.0]
– Phase: [0, Pi]
– x: sampled uniformly from [-5.0, 5.0]
– f(x): 2 hidden layers of size 40 with ReLU
– K={5,10,20}
› Comparison
– Pretraining on all of the tasks
› Regress to random sine functions
› Fine-tune with gradient descent on the K provided points
– Oracle
– Additional multi-task and adaptation methods

10. Regression

11. Classification
› Omniglot dataset
– 20 instances of 1623 characters from 50 different alphabets
– Each instance was drawn by a different person
– Randomly selected 1200 characters for training, and the
remaining for testing
› MiniImagenet dataset
– 64 training classes, 12 validation classes, and 24 test classes
› N way classification with 1 or 5 shots

12. Classification

13. Reinforcement learning
› rllab benchmark suite
› Neural network policy with two hidden layers of size 100
with ReLU
› Gradients updates are computed using vanilla policy
gradients (REINFORCE) and trust-region policy (TRPO)
optimization as meta-optimizer
› Comparison
– Pretraining one policy on all of the tasks and fine-tuning
– Training a policy from randomly initialized weights
– Oracle policy

14. Reinforcement learning
› 2d navigation

15. Reinforcement learning
› Locomotion
– High-dimensional locomotion tasks with the MuJoCo simulator

16. Reinforcement learning

17. Advanced researches
› Meta-SGD: Learning to learn quickly for few-shot learning,
Li et al, Sep 2017
› Recasting gradient-based meta-learning as hierarchical
Bayes, Grant et al, ICLR 2018
› Gradient-based meta-learning with learned layerwise
metric and subspace, Lee et al, ICML 2018
› Probabilistic Model-agnostic meta learning, Finn et al, Jun
2018
› Bayesian Model-Agnostic Meta-Learning, Kim et al, Jun
2018

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