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  • PAPER NOTES
  • Meta-Learning with Implicit Gradient
  • DARTS: Differentiable Architecture Search
  • Meta-Learning of Neural Architectures for Few-Shot Learning
  • Towards Fast Adaptation of Neural Architectures with Meta Learning
  • Editable Neural Networks
  • ANIL (Almost No Inner Loop)
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  • Learning to learn by gradient descent by gradient descent
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    • Incremental Few Shot Learning with Attention Attractor Network
    • Learning Steady-States of Iterative Algorithms over Graphs
      • Experiments
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    • Meta-Learning with Shared Amortized Variational Inference
    • Concept Learners for Generalizable Few-Shot Learning
    • Progressive Graph Learning for Open-Set Domain Adaptation
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    • Learning to stop while learning to predict
    • Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift
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  • Oct
    • Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization
    • Network Architecture Search for Domain Adaptation
    • Continuous Meta Learning without tasks
    • Learning Causal Models Online
    • Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples
    • Conditional Neural Progress (CNPs)
    • Reviving and Improving Recurrent Back-Propagation
    • Meta-Q-Learning
    • Learning Self-train for semi-supervised few shot classification
    • Watch, Try, Learn: Meta-Learning from Demonstrations and Rewards
  • Nov
    • Neural Process
    • Adversarially Robust Few-Shot Learning: A Meta-Learning Approach
    • Learning to Adapt to Evolving Domains
  • Tutorials
    • Relax constraints to continuous
    • MAML, FO-MAML, Reptile
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  1. Oct

Learning Self-train for semi-supervised few shot classification

NeurIPS 19 11/3/2020

PreviousMeta-Q-LearningNextWatch, Try, Learn: Meta-Learning from Demonstrations and Rewards

Last updated 4 years ago

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Motivation

This paper is not hard to understand, the slide: () has been very clear.

Notations:

  • Φss\Phi_{ss}Φss​ : feature extractor of base learner, one of meta-parameters

  • θ\thetaθ : final layer classifier of base learner

  • θ′\theta'θ′ : initialization parameters of θ\thetaθ , one of meta-parameters

  • Φswn\Phi_{swn}Φswn​ : weights of soft-weighting network, one of meta-parameters

Inner loop:

  1. Pseudo-labeling

  2. Cherry-picking: (hard selection, soft weighting)

  3. Self-training: re-training (S+R), fine-tuning (S)

Outer loop:

  1. update Φswn\Phi_{swn}Φswn​ after re-training using the validation loss on query set based on θm\theta_mθm​

  2. update [Φss,θ′][\Phi_{ss}, \theta'][Φss​,θ′] after fine-tuning using the validation loss on query set based on θT\theta_TθT​

Reference

https://drive.google.com/file/d/151ZyvJK77nPJ36LA2gdk3S--8caXS43-/view
https://papers.nips.cc/paper/9216-learning-to-self-train-for-semi-supervised-few-shot-classification.pdf
https://github.com/xinzheli1217/learning-to-self-train
https://drive.google.com/file/d/151ZyvJK77nPJ36LA2gdk3S--8caXS43-/view
https://qianrusun.com/