2. Contents
1. Meta-Learning Problem Setup (Definition & Goal)
2. Prior Work
3. Model Agnostic Meta Learning
1. Characteristics
2. Intuition
4. Approach
1. Supervised Learning
2. Gradient of Gradient
3. Reinforcement Learning
5. Experiment
6. Discussion
3. Meta-Learning Problem Set-up
• Definition of meta-learning:
• Learner is trained by meta-learner to be able to learn on many different
tasks.
• Goal:
• Learner quickly learn new tasks from a small amount of new data.
1. Problem Set-up
12. Prior Work
• Learning an update function or update rule
• LSTM optimizer
(Learning to learn by gradient descent by gradient descent)
• Meta LSTM optimizer
(Optimization as a model for few-shot learning)
• Few shot (or meta) learning for specific tasks
• Generative modeling (Neural Statistician)
• Image classification (Matching Net., Prototypical Net.)
• Reinforcement learning
(Benchmarking deep reinforcement learning for continuous control)
2. Prior Work
13. Prior Work
• Memory-augmented meta-learning
• Memory-augmented models on many tasks
(Meta-learning with memory-augmented neural networks)
• Parameter initialization of deep networks
• Exploring sensitivity while maintaining Internal representation
(Overcoming catastrophic forgetting in NN)
• Data-dependent Initializer
(Data-Dependent Initialization of CNN)
• learned initialization
(Gradient-based hyperparameter optimization through reversible learning)
2. Prior Work
14. Model-Agnostic Meta-Learning (MAML)
• Goal:
• Quickly adapt to new tasks on distribution
with only small amount of data and
with only a few gradient steps,
even one gradient step.
• Learner:
• Learn a new task by using a single gradient step.
• Meta-learner:
• Learn a generalized parameter initialization of model.
3. Model-Agnostic Meta-Learning (MAML)
15. Characteristics of MAML
• The MAML learner’s weights are updated using the gradient,
rather than a learned update.
• Not need additional parameters nor require a particular learner
architecture.
• Fast adaptability through good parameter initialization
• Explicitly optimizes to learn Internal representation
(i.e. suitable for many tasks).
• Maximizes sensitivity of new task losses to the model parameters.
3. Model-Agnostic Meta-Learning (MAML)
16. Characteristics of MAML
• Model-agnostic (No matter whatever model is)
• Classification & regression with differentiable losses, Policy gradient RL.
• The model should be parameterized.
• No other assumptions on the form of the model.
• Task-agnostic (No matter whatever task is)
• Adopted all knowledge-transferable tasks.
• No other assumption is required.
3. Model-Agnostic Meta-Learning (MAML)
17. • Some internal representations
are more transferrable than others.
• Desired model parameter set is θ such that:
Applying one (or a small # of) gradient step to θ
on a new task will produce maximally effective behavior.
→ Find θ that commonly decreases loss of each task
after adaptation.
Intuition of MAML
3. Model-Agnostic Meta-Learning (MAML)
18. Supervised Learning
• Notations
• Model: (Model-agnostic)
• Task distribution:
• For each Task:
• Data distribution: q (K samples are drawn from q)
• Loss function:
• MSE for Regression
• Cross entropy for classification
• Any other differentiable loss functions can be used.
4. Approach
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20. • From line 10 in Algorithm 2,
(Recall: )
Gradient of Gradient
4. Approach
21. • From line 10 in Algorithm 2,
(Recall: )
( is differentiable)
Gradient of Gradient
4. Approach
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22. • From line 10 in Algorithm 2,
(Recall: )
( is differentiable)
Gradient of Gradient
4. Approach
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23. • From line 10 in Algorithm 2,
(Recall: )
( is differentiable)
Gradient of Gradient
4. Approach
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24. • From line 10 in Algorithm 2,
(Recall: )
( is differentiable)
Gradient of Gradient
4. Approach
Calculation of Hessian matrix is required.
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25. • From line 10 in Algorithm 2,
(Recall: )
( is differentiable)
Gradient of Gradient
4. Approach
Calculation of Hessian matrix is required.
→ MAML suggest 1st order approximation.
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27. 1st Order Approximation
• Update rule of MAML:
4. Approach
This part needs calculating Hessian.
Hessian makes MAML be slow.
28. 1st Order Approximation
• Update rule of MAML:
• Update rule of MAML with 1st order approximation:
4. Approach
29. 1st Order Approximation
• Update rule of MAML:
• Update rule of MAML with 1st order approximation:
4. Approach
(Regard as constant)
30. • From line 10 in Algorithm 2,
(Recall: )
( is differentiable)
Recall: Gradient of Gradient
4. Approach
In 1st order approximation,
we regard this as identity matrix I.
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31. Reinforcement Learning
• Notations
• Policy of an agent: such that a_t sim f_theta (x_t)
• Task distribution:
• For each Task:
• Episode:
• Transition distribution: q (K trajectories are drawn from q and f)
• Episode length: H
• Loss function: = expectation of sum of rewards
4. Approach
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33. Experiment on Few-Shot Classification
• Omniglot (Lake et al., 2012)
• 50 different alphabets, 1623 characters.
• 20 instances for each characters were drawn by 20 different people.
• 1200 for training, 423 for test.
• Mini-Imagenet (Ravi & Larochelle, 2017)
• Classes for each set: train=64, validation=12, test=24.
5. Experiment
35. Effect of 1st Order Approximation
• In 1st order approximation, computation is roughly 33% better.
• Performances are similar.
5. Experiment
36. Effect of 1st Order Approximation
• In 1st order approximation, computation is roughly 33% better.
• Performances are similar.
5. Experiment
Performances are not that different.
37. Effect of 1st Order Approximation
• In 1st order approximation, computation is roughly 33% better.
• Performances are similar.
5. Experiment
Some performance is even better.
38. Experiments on Regression
• Sinusoid function:
• Amplitude (A) and phase (ɸ) are varied between tasks
• A in [0.1, 0.5]
• ɸ in [0, π]
• x in [-5.0, 5.0]
• Loss function: Mean Squared Error (MSE)
• Regressor: 2 hidden layers with 40 units and ReLU
• Training
• Use only 1 gradient step for learner
• K = 5 or 10 example (5-shot learning or 10-shot learning)
• Fixed step size (ɑ=0.01) for Adam optimizer.
5. Experiment
39. Results of 10-Shot Learning Regression
5. Experiment
[MAML] [Pretraining + Fine-tuning]
The red line is ground truth.
Fit this sine function with only few (10) samples.
40. Results of 10-Shot Learning Regression
5. Experiment
Above plots are the pre-trained function of two models.
(The prediction of meta-parameter of MAML,
The prediction of co-learned parameter of vanilla multi-task learning)
[MAML] [Pretraining + Fine-tuning]
41. Results of 10-Shot Learning Regression
5. Experiment
After 1 gradient step update.
[MAML] [Pretraining + Fine-tuning]
42. Results of 10-Shot Learning Regression
5. Experiment
[MAML] [Pretraining + Fine-tuning]
After 10 gradient step update.
43. Results of 10-Shot Learning Regression
5. Experiment
* step size 0.02
In the case of MAML, they quickly adapted to new samples.
But, adaptation failed in the case of pretraining model.
[MAML] [Pretraining + Fine-tuning]
44. Results of 5-Shot Learning Regression
5. Experiment
* step size 0.01
[MAML] [Pretraining + Fine-tuning]
In the 5-shot learning, the difference is pervasive.
45. Results of 5-Shot Learning Regression
5. Experiment
* step size 0.01
In the 5-shot learning, the difference is pervasive.
Good predictions are also made for ranges not particularly seen.
[MAML] [Pretraining + Fine-tuning]
46. MAML Needs Only One Gradient Step
5. Experiment
Vanilla pretrained model adapted slowly,
but, the MAML method quickly adapted even in one gradient step.
47. MAML Needs Only One Gradient Step
5. Experiment
Vanilla pretrained model adapted slowly,
but, the MAML method quickly adapted even in one gradient step.
Meta parameter theta of MAML is more sensitive
than pre-updated theta of vanilla pretrained model.
48. MSE of the Meta-Parameters
• The performance of the-meta
parameters was not improved
much in training.
• However, the performance of
the single gradient updated
parameters
started on meta-parameters
improved as training progressed.
5. Experiment
49. Experiments on Reinforcement Learning
• rllab benchmark suite
• Neural network policy with two hidden layers of size 100 with ReLU
• Gradients updates are computed using vanilla policy gradient
(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
5. Experiment
53. Why 1st Order Approximation Works?
• [Montufar14] The objective function of ReLU network is locally linear.
• So as the other piecewise linear activations (maxout, leaky relu, ...) are
used.
• Because, any composite function with linear or piecewise linear
functions should be locally linear.
•
6. Discussion
magnification
Solid: Shallow network with 20 hidden units
Dotted: deep network (2 hidden layers) with 10 hidden units at each layer
54. Why 1st Order Approximation Works?
• [Montufar14] The objective function of ReLU network is locally linear.
• So as the other piecewise linear activations (maxout, leaky relu, ...) are
used.
• Because, any composite function with linear or piecewise linear
functions should be locally linear.
• Hence, the Hessian matrix is almost zero.
= Hessian matrix is meaningless.
6. Discussion
magnification
Solid: Shallow network with 20 hidden units
Dotted: deep network (2 hidden layers) with 10 hidden units at each layer
55. Why 1st Order Approximation Works?
• If we change the activation function from ReLU to sigmoid?
6. Discussion
56. Why 1st Order Approximation Works?
• If we change the activation function from ReLU to sigmoid?
6. Discussion
ReLU sigmoid
w/o 1st order approx. 0.1183 0.1579
w 1st order approx. 0.1183 0.1931
ReLU sigmoid
w/o 1st order approx. 61 96
w 1st order approx. 53 (13% better) 52 (46% better)
[ MSE Comparison ]
[ Learning Time Comparison (minutes) ]
57. Why the Authors Focused on Deep Networks?
• The authors said their model (MAML) is model-agnostic.
• But, why they focused on deep networks?
• If it is not deep networks, what kind of problem can occur?
6. Discussion
58. Why the Authors Focused on Deep Networks?
• Simple linear regression model (y = θx) can be used in the MAML.
• Parametrized, differentiable loss function.
6. Discussion
59. Why the Authors Focused on Deep Networks?
• Simple linear regression model (y = θx) can be used in the MAML.
• Parametrized, differentiable loss function.
• Let
6. Discussion
60. Why the Authors Focused on Deep Networks?
• Simple linear regression model (y = θx) can be used in the MAML.
• Parametrized, differentiable loss function.
• Let
• Then, meta parameter θ will be 1.25.
6. Discussion
61. Why the Authors Focused on Deep Networks?
• Let’s extend the setting.
• The sample is drawn from for the task .
• Then the meta parameter .
6. Discussion
62. Why the Authors Focused on Deep Networks?
• Let’s extend the setting.
• The sample is drawn from for the task .
• Then the meta parameter .
• The MAML use fixed learning rate.
• More than 2 gradient steps are needed when is far from .
6. Discussion
63. Why the Authors Focused on Deep Networks?
• Let’s extend the setting.
• The sample is drawn from for the task .
• Then the meta parameter .
• The MAML use fixed learning rate.
• More than 2 gradient steps are needed when is far from .
• However, because the dimension of the deep model is large,
there may be a meta parameter
that can quickly adapt to any task parameter .
• This is why we think the authors focused on the deep models.
6. Discussion
64. • However, using the deep model doesn’t eliminate the problem
completely.
• The authors mentioned about the generalization problem for
extrapolated tasks.
• The closer the task is to out-of-distribution, the worse the performance.
• How can we solve this problem?
Why the Authors Focused on Deep Networks?
6. Discussion
65. Update Meta Parameters with N-steps
• The MAML updates using only one gradient step
when updating the meta parameter.
• The authors say that you can update meta parameters using n
gradient steps.
6. Discussion
66. Update Meta Parameters with N-steps
• Will it show better performance to update with N gradient steps?
• If so, why the authors didn’t use this method in experiments?
6. Discussion
67. Update Meta Parameters with N-steps
• Will it show better performance to update with N gradient steps?
• If so, why the authors didn’t use this method in experiments?
• If we apply this method to MAML, the MAML model will find a
sensitive meta parameter in the N-step update.
6. Discussion
68. Update Meta Parameters with N-steps
• Will it show better performance to update with N gradient steps?
• If so, why the authors didn’t use this method in experiments?
• If we apply this method to MAML, the MAML model will find a
sensitive meta parameter in the N-step update.
• Also, we should calculate (n + 1)-order partial derivatives.
• (c.f.) Hessian matrix is 2-order partial derivatives.
• The computation of (n + 1)-order partial derivatives is
computationally expensive.
6. Discussion
69. Update Meta Parameters with N-steps
• How about using 1st order approximation with ReLU activation
and updating meta parameter with N-steps?
6. Discussion
70. Update Meta Parameters with N-steps
• N-step(s) updates with 1st order approximation.
• Of course, we use ReLU for activation function.
6. Discussion
1-step 2-steps 3-steps
Learning Time 53 73 92
71. Update Meta Parameters with N-steps
• N-step(s) updates with 1st order approximation.
• Of course, we use ReLU for activation function.
6. Discussion
1-step 2-steps 3-steps
Learning Time 53 73 92
the performance of the meta-parameter
getting worser when n grows.
72. Update Meta Parameters with N-steps
• N-step(s) updates with 1st order approximation.
• Of course, we use ReLU for activation function.
6. Discussion
1-step 2-steps 3-steps
Learning Time 53 73 92
Final performance may be unrelated to N.
(More experiments are needed for confirming this issue.)
73. Unsupervised Learning
• The authors said that the MAML is model agnostic.
• They showed the MAML is worked on supervised learning.
• How about unsupervised learning?
6. Discussion
74. • The authors said that the MAML is model agnostic.
• They showed the MAML is worked on supervised learning.
• How about unsupervised learning?
• We can use autoencoder.
• Autoencoder can make unsupervised learning
be regarded as supervised learning.
Unsupervised Learning
6. Discussion
75. • MAML is really slow algorithm even with 1st order approximation.
• How can we make the MAML framework be faster?
How to Make the MAML be faster?
6. Discussion
76. • MAML is really slow algorithm even with 1st order approximation.
• How can we make the MAML framework be faster?
• [Zhenguo 2017] suggests
optimizing learning rate alpha.
• Instead of putting alpha
as a hyperparameter,
train the vector alpha
as the parameters
with other parameters.
• It outperforms the vanilla MAML.
How to Make the MAML be faster?
6. Discussion
77. Application
• The MAML is good for a few-shot learning problem.
• Can you guess any applications of few-shot learning problem?
6. Discussion
78. Application
• The MAML is good for a few-shot learning problem.
• Can you guess any applications of few-shot learning problem?
• We think one of the most effective few-shot learning problem is
real estate price prediction.
• There are a lot of transactions related to big apartments.
• But, there are many transactions in small apartments.
So, it is difficult to predict.
• Using MAML seems to solve the problem of insufficient sample size of
small apartments.
• Also, other problems would be solved by MAML.
6. Discussion
80. Reference
• Finn, Chelsea, Pieter Abbeel, and Sergey Levine. "Model-agnostic
meta-learning for fast adaptation of deep networks." arXiv preprint
arXiv:1703.03400 (2017).
• Montufar, Guido F., et al. "On the number of linear regions of deep
neural networks." Advances in neural information processing
systems. 2014.
• Baldi, Pierre. "Autoencoders, unsupervised learning, and deep
architectures." Proceedings of ICML workshop on unsupervised and
transfer learning. 2012.
• Li, Zhenguo, et al. "Meta-sgd: Learning to learn quickly for few shot
learning." arXiv preprint arXiv:1707.09835 (2017)