목록few-shot learning (3)

iMTE

Meta-learning with Implicit Gradients (NIPS 2019) [1]

Meta-learning with Implicit Gradients [1] https://papers.nips.cc/paper/2019/hash/072b030ba126b2f4b2374f342be9ed44-Abstract.html IntroductionMeta-learning의 frame에서 bi-level optimization procedure는 다음으로 나누어진다.1) inner optimization : 주어진 task에 base learner가 학습하는 과정2) outer optimization : 여러 tasks 들에서 meta learner가 학습하는 과정MAML, DAML, Reptile 등의 방법이 optimization-based methods에 속한다. (Hands-on one-shot..
Deep learning study/Meta-Learning 2020. 12. 24. 15:23
Meat-Learning 8. Meta-learning for semi-supervised few-shot classification

Meta-learning for semi-supervised few-shot classification (ICLR, 2018) Abstract"In this work, we advance this few-shot classification paradigm towards a scenario where unlabeled examples are also available within each episode....To address this paradigm, we propose novel extensions of Prototypical Networks that are augmented with the ability to use unlabeled examples when producing prototypes."U..
Deep learning study/Meta-Learning 2019. 5. 25. 16:13
Meta-Learning 2. Optimization as a model for few-shot learning

Paper : OPTIMIZATION AS A MODEL FOR FEW-SHOT LEARNINGDownload : https://openreview.net/forum?id=rJY0-Kcll¬eId=ryq49XyLgAbstract큰 data domain에서 deep neural networks는 큰 성공을 보여주었지만, few-shot learning tasks에서는 성능이 별로 좋지 않았다. (이는 각 class의 매우 적은 example을 보고 빠르게 일반화를 해야하기 때문이다.) 큰 network (e.g., high capacity classifier)에서 gradient-based optimization은 많은 example에 많은 iteration을 해야 성능이 잘나온다는 것이 일반적인 믿..
Deep learning study/Meta-Learning 2019. 4. 20. 19:47
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