zhoumu53 / few_shot_learning

Awesome papers in few-shot learning/one-shot learning.

There are some papers/articles need to read.

Keywords:

• Meta learning
• Metric learning
• Few-shot learning
• One-shot learning
• Zero-shot learning
• GAN
• VAE

Metric learning based approaches

Papers:

These are the methods based on metric distance for few-shot learning.

Siamese network/Triplet network

• Siamese Neural Networks for One-Shot Image Recognition
• FaceNet: A Unified Embedding for Face Recognition and Clustering
• Deep Metric Learning Using Triplet Network

Matching network

Some papers from Google DeepMind, should read in the order.

• Neural Turing Machines
• One-shot Learning with Memory-Augmented Neural Networks
• Matching Networks for One Shot Learning

Prototypical network

• Prototypical Networks for Few-shot Learning

Graph network

Papers:

• The Graph Neural Network Model
• Few-shot Learning with Graph Neural Networks

Related articles:

• GRAPH CONVOLUTIONAL NETWORKS

Relation Network

• Learning to Compare: Relation Network for Few-Shot Learning

Others

• Feature Generating Networks for Zero-ShsoetenLearning
• Large Margin Few-Shot Learning
• Label-Embedding for Image Classification
• Evaluation of Output Embeddings for Fine-Grained Image Classification
• Learning Deep Representations of Fine-Grained Visual Descriptions
• Transductive Unbiased Embedding for Zero-Shot Learning
• Related articles: From Zero to Hero: Shaking Up the Field of Zero-shot Learning
• Recent Advances in Zero-shot Recognition
• An embarrassingly simple approach to zero-shot learning

Articles

• What Is Zero-Shot Learning?

Meta-Learning(learning to learn) based approaches

These are the methods based on meta learning for few-shot learning.

The most popular two are MAML and Reptile.

Papers

• On First-Order Meta-Learning Algorithms
• Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
• META-LEARNING FOR SEMI-SUPERVISED FEW-SHOT CLASSIFICATION
• Meta-Learning with Latent Embedding Optimization

Articles

This is the overview of learning to learn written by berkeley AI research.

• Learning to Learn

This article introduced the meta-learning by animation clearly, one of the best explaination of meta-learning I think.

• From zero to research — An introduction to Meta-learning

This article explain the Reptile, which is from the paper: On First-Order Meta-Learning Algorithms.

• Reptile: A Scalable Meta-Learning Algorithm

Generative and augmentation-based approaches

It's a new paper from NIPS 2018, by IBM research AI.

• ∆-encoder: an effective sample synthesis method for few-shot object recognition
• MetaGAN: An Adversarial Approach to Few-Shot Learning
  • It's better to read the paper about MAML, Relation Network, DAGAN before.
• Data Agumentation Generative Adversarial Networks