최근 machine learning 분야에서 활발히 연구되고 있는 meta-learning은 기존의 Gradient-descent 기반 학습 방법의 한계점으로 지적되는 엄청난 규모의 데이터 요구량 문제를 해결하기 위해 연구되는 분야로 학습 모델이 수 샘플으로도 충분한 학습 성능을 낼 수 있도록 하는 학습 기법이다. 메타 러닝 기법 중에서 Model-Agnostic Meta-Learning (MAML)은 학습 대상 모델의 구조와 상관없이 새로운 gradient-descent based algorithm을 통해 classification, reinforcement learning 임무를 빠른 시간 안에 높은 성능을 가지는 모델으로 학습하는 것이 실제로 가능하다고 보여주었다. 하지만 MAML은 image segmentation과 같이 복잡한 학습 네트워크 모델을 가지는 일에서는 효과적인 성능을 보여주지 못한다. 따라서 본 발표에서는 segmentation에 적용할 수 있는 MAML 기반 학습법에 대해 고찰하고, 특히 segmentation 네트워크를 re-training, transfer-learning와 같이 fine-tuning해야할 때 쓸 수 있는 meta-learning 기법을 소개하고자 한다. 제안된 기법은 active meta-tune이라 부르며, classification과 달리 복잡한 구조를 가지는 segmentation을 잘 수행하기 위해 meta-learning을 통해 학습하는 학습 데이터의 순서를 active learning 기반 알고리즘으로 정해주는 기술이다. 그러므로 본 발표에서는 active learning과 meta-learning이 어떻게 결합될 수 있는 지에 대한 이론적 배경과 active meta-tune의 알고리즘, 실제 적용 분야에 대하여 다룰 것이다.

1. Learning to learn unlearned feature
for segmentation
Sungyeob Han
Communication and Machine Learning Lab.
Seoul National University

2. Introduction
• How to transfer with few samples?
Primary cancer Brain metastasis

3. Outline
1. Training segmentation network
2. Meta-learning
3. Active learning
4. Active meta-tune
5. Applications

5. Fully convolutional networks
• take input of arbitrary size and produce correspondingly-
sized output
• a feed-forward propagation predicts the labels
• end-to-end, pixel-to-pixel
Long, Jonathan, Evan Shelhamer, and Trevor Darrell. "Fully convolutional networks for semantic segmentation."

6. Pyramid Scene Parsing Network
pyramid parsing module : harvest different sub-region representations
concatenation : upsampling and concatenation layers
Zhao, Hengshuang, et al. "Pyramid scene parsing network."

7. Zhao, Hengshuang, et al. "Pyramid scene parsing network."

8. Details in training segmentation network
• Fast feed-forward time (FCN-based)
• Given the pre-trained encoding parameters (VGGNet), fine-tuning
in stages takes 36 hours on a single GPU.
• Ambiguity : object structure, sparse label
• constrained categories
average loss gives blurry gradient for each
category information

9. Outline
1. Training segmentation network
2. Meta-learning
3. Active learning
4. Active meta-tune
5. Applications

10. Learning to learn
• A key aspect of intelligence : versatility
• the capability of doing many different things.
• Meta-learning
• As known as ”learning to learn”
• learn how to learn new tasks faster by reusing previous experience

11. Few-shot Learning
• In 2015, Brenden et al. show how to learn new concepts from one
or a few instances of that concept.
• learn to learn from
a few examples
• Omniglot
• 1623 character classes
• each with 20 examples
Lake, Brenden M., Ruslan Salakhutdinov, and Joshua B. Tenenbaum. "Human-level concept learning through probabilistic program
induction."

12. Meta-learner
Ravi, Sachin, and Hugo Larochelle. "Optimization as a model for few-shot learning.“

13. Meta-learning set-up for few-shot image classification
• 1-shot, 5-class classification task
• one example from each of 5 classes

14. Model agnostic meta-learning
• Motivation : ambiguity on new task
• From a dynamical systems standpoint
• Maximize the sensitivity of the loss functions of new tasks

15. Model Agnostic Meta-Learning
• A model 𝑓
• Maps observation 𝐱 to outputs 𝐚
• Each task 𝒯 = ℒ 𝐱1, 𝐚1, ⋯ , 𝐱 𝐻, 𝐚 𝐻 , 𝑞 𝐱1 , 𝑞 𝐱 𝑡+1|𝐱 𝑡, 𝐚 𝑡 , 𝐻
• Loss, Initial distribution, a transition dist., episode length
• ℒ 𝐱1, 𝐚1, ⋯ , 𝐱 𝐻, 𝐚 𝐻
• Task-specific feedback
• A misclassification loss

16. Algorithm : MAML
• K-shot learning
• Learn a new Task 𝓣𝒊 sampled from 𝒑 𝓣
from only K samples sampled from 𝒒𝒊
• Feedback ℒ 𝑇 𝑖
generated by 𝒯𝑖
• Train with K samples
• Test on new samples from 𝒯𝑖
• improved by considering how the test error
on new data from 𝑞𝑖 changes with respect to
the parameters.
• the test error on sampled tasks 𝒯𝑖 serves as
the training error of the meta-learning
process.

17. Algorithm : MAML
• A model with parameters 𝑓𝜃 , adapting to an
new task 𝒯𝑖 ∶ 𝜃 → 𝜃𝑖′
𝜃𝑖
′
= 𝜃 − 𝛼𝛻𝜃ℒ 𝒯𝑖
(𝑓𝜃)
• Meta-objective : the model parameters are
trained by optimizing for performance of 𝑓 𝜃′
min
𝜃
෍
𝒯𝑖~𝑝(𝒯)
ℒ 𝒯𝑖
𝑓 𝜃𝑖
′ = ෍
𝒯𝑖~𝑝(𝒯)
ℒ 𝒯𝑖
𝑓𝜃−𝛼𝛻 𝜃ℒ 𝑇 𝑖
(𝑓 𝜃)
• The meta-optimization across tasks
𝜃 ← 𝜃 − 𝛽𝛻𝜃 ෍
𝒯𝑖~𝑝(𝒯)
ℒ 𝒯𝑖
𝑓 𝜃𝑖
′

18. Experimental Evaluation : Regression

19. Experimental Evaluation : Supervised classification

20. Experimental Evaluation : RL

21. Desinging of meta-learning
Levine, Sergey, and Chelsea Finn, “Meta-learning frontiers: universal, uncertain, and unsupervised.”

22. Meta-learning with ambiguity
Levine, Sergey, and Chelsea Finn, “Meta-learning frontiers: universal, uncertain, and unsupervised.”

23. Meta-learning with ambiguity
Levine, Sergey, and Chelsea Finn, “Meta-learning frontiers: universal, uncertain, and unsupervised.”

24. Outline
1. Training segmentation network
2. Meta-learning
3. Active learning
4. Active meta-tune
5. Applications

25. Active learning
• pool-based active learning
• queries are selected from
a large pool of unlabeled
instances
• uncertainty sampling
query strategy
• select the instance in the
pool about which the
model is least certain
how to label
Settles, Burr “Active learning literature survey.”

26. pool-based active learning
• A toy example : logistic regression (400 instances)
• (b) : 30 labeled instances randomly drawn : 70% accuracy
• (c) : 30 actively queried instances using uncertainty sampling : 90%

27. Uncertainty sampling
• uncertainty measures
Mussmann, Stephen, and Percy Liang. "On the Relationship between Data Efficiency and Error for Uncertainty Sampling.”

28. How can me measure the uncertainty of networks?
• a small dataset for a new task can simply be too ambiguous to
acquire a single model
Kendall, Alex, and Yarin Gal. "What uncertainties do we need in bayesian deep learning for computer vision?.“

29. Outline
1. Training segmentation network
2. Meta-learning
3. Active learning
4. Active meta-tune
5. Applications

30. Catastrophic forgetting

31. Generate training tasks good task
bad task
𝑻 𝒈𝒐𝒐𝒅
𝑻 𝒃𝒂𝒅
Han, Sungyeob, et al. “Learning to Learn Unlearned Feature for Brain Tumor Segmentation.”

32. Active meta-tune
Han, Sungyeob, et al. “Learning to Learn Unlearned Feature for Brain Tumor Segmentation.”

33. Active meta-tune
Han, Sungyeob, et al. “Learning to Learn Unlearned Feature for Brain Tumor Segmentation.”

34. Active meta-tune
𝜶 𝑻
𝜷 𝑻
Han, Sungyeob, et al. “Learning to Learn Unlearned Feature for Brain Tumor Segmentation.”

35. Outline
1. Training segmentation network
2. Meta-learning
3. Active learning
4. Active meta-tune
5. Applications

36. Brain tumor segmentation
FLAIR Ground Truth Proposed ModelBaseline
• 3D FCN on High grade glioma (BRATS)
Mean Dice Score (std)
Whole Active Core
Baseline 0.72
(0.18)
0.54
(0.25)
0.44
(0.24)
Proposed 0.77
(0.13)
0.57
(0.23)
0.51
(0.25)
Convolution Deconvolution

37. Types of brain tumor – size and location
• Brain tumor treatment options depend on the type of brain tumor
you have, as well as its size and location.
• Types
• Acoustic neuroma, Astrocytoma, Brain metastases
• Choroid plexus carcinoma, Craniopharyngioma
• Embryonal tumors, Ependymoma, Glioblastoma
• Glioma, Medulloblastoma, Meningioma
• Oligodendroglioma, Pediatric brain tumors
• Pineoblastoma, Pituitary tumors
early stage of brain metastasis

38. Motivation on brain metastasis segmentation
• The target feature is different
• Too many small tumors : automation needed!
x40 slides
for 4 seq.

39. Active meta-tune on brain metastasis segmentation
Convolution Deconvolution
Active learning
based samplingMAML
task
arrang
ement
Han, Sungyeob, et al. “Learning to Learn Unlearned Feature for Brain Tumor Segmentation.”

40. Experimental Results – pretrained

41. Experimental Results – 1 step update

42. Experimental Results – 5 step updates

43. Experimental Results
Results on training dataset
Method Dice Score (mean, std)
Baseline 0.66 (on BRaTS dataset)
Naive 0.33 ± 0.3413
Passive 0.41 ± 0.2752
Active 0.45 ± 0.2317
DSC result for enhancing tumor
Results on validation dataset

44. Conclusion
• Transfer learning method from high grade glioma to brain
metastasis
• Learn unlearned features without forgetting the original task in
brain tumor segmentation
• Show the generalization effect in target domain segmentation
(brain metastasis)

45. Questions?
Segmentation
Long, Jonathan, Evan Shelhamer, and Trevor Darrell. "Fully convolutional networks for semantic segmentation."
Zhao, Hengshuang, et al. "Pyramid scene parsing network.“
Kendall, Alex, and Yarin Gal. "What uncertainties do we need in bayesian deep learning for computer vision?.“
Meta-learning
Finn, Chelsea, Pieter Abbeel, and Sergey Levine. "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks.“
Finn, Chelsea, Kelvin Xu, and Sergey Levine. "Probabilistic Model-Agnostic Meta-Learning.“
Ravi, Sachin, and Hugo Larochelle. "Optimization as a model for few-shot learning.“
Lake, Brenden M., Ruslan Salakhutdinov, and Joshua B. Tenenbaum. "Human-level concept learning through probabilistic program induction."
Active learning
Settles, Burr “Active learning literature survey.”
Mussmann, Stephen, and Percy S. Liang. "Uncertainty Sampling is Preconditioned Stochastic Gradient Descent on Zero-One Loss.“
Mussmann, Stephen, and Percy Liang. "On the Relationship between Data Efficiency and Error for Uncertainty Sampling.”
Applications
Menze, Bjoern H., et al. "The multimodal brain tumor image segmentation benchmark (BRATS).“
Han, Sungyeob, et al. “Learning to Learn Unlearned Feature for Brain Tumor Segmentation.”