Few-Shot Image Classification
Few-shot image classification is the task of doing image classification with only a few examples for each category (typically < 6 examples).
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Greatest papers with code
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
ICML 2017
• cbfinn/maml
• 
We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning.
On First-Order Meta-Learning Algorithms
This paper considers meta-learning problems, where there is a distribution of tasks, and we would like to obtain an agent that performs well (i. e., learns quickly) when presented with a previously unseen task sampled from this distribution.
#5 best model for
Few-Shot Image Classification
on OMNIGLOT - 1-Shot Learning
Prototypical Networks for Few-shot Learning
NeurIPS 2017
• orobix/Prototypical-Networks-for-Few-shot-Learning-PyTorch
• 
We propose prototypical networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each new class.
FEW-SHOT IMAGE CLASSIFICATION ONE-SHOT LEARNING ZERO-SHOT LEARNING
A Closer Look at Few-shot Classification
ICLR 2019
• wyharveychen/CloserLookFewShot
•
Few-shot classification aims to learn a classifier to recognize unseen classes during training with limited labeled examples.
#3 best model for
Few-Shot Image Classification
on Mini-ImageNet - 5-Shot Learning
DOMAIN GENERALIZATION FEW-SHOT IMAGE CLASSIFICATION FEW-SHOT LEARNING
A Closer Look at Few-shot Classification
ICLR 2019
• wyharveychen/CloserLookFewShot
•
Few-shot classification aims to learn a classifier to recognize unseen classes during training with limited labeled examples.
#3 best model for
Few-Shot Image Classification
on CUB 200 5-way 5-shot
DOMAIN GENERALIZATION FEW-SHOT IMAGE CLASSIFICATION FEW-SHOT LEARNING
Dynamic Few-Shot Visual Learning without Forgetting
CVPR 2018
• gidariss/FewShotWithoutForgetting
•
In this context, the goal of our work is to devise a few-shot visual learning system that during test time it will be able to efficiently learn novel categories from only a few training data while at the same time it will not forget the initial categories on which it was trained (here called base categories).
#4 best model for
Few-Shot Image Classification
on Mini-ImageNet - 1-Shot Learning
FEW-SHOT IMAGE CLASSIFICATION OBJECT RECOGNITION ONE-SHOT LEARNING
Matching Networks for One Shot Learning
NeurIPS 2016
• AntreasAntoniou/MatchingNetworks
•
Our algorithm improves one-shot accuracy on ImageNet from 87. 6% to 93. 2% and from 88. 0% to 93. 8% on Omniglot compared to competing approaches.
#4 best model for
Few-Shot Image Classification
on OMNIGLOT - 1-Shot Learning
FEW-SHOT IMAGE CLASSIFICATION LANGUAGE MODELLING METRIC LEARNING ONE-SHOT LEARNING
How to train your MAML
ICLR 2019
• AntreasAntoniou/HowToTrainYourMAMLPytorch
•
The field of few-shot learning has recently seen substantial advancements.
#6 best model for
Few-Shot Image Classification
on Mini-ImageNet - 1-Shot Learning
Meta-Learning with Differentiable Convex Optimization
CVPR 2019
• kjunelee/MetaOptNet
•
We propose to use these predictors as base learners to learn representations for few-shot learning and show they offer better tradeoffs between feature size and performance across a range of few-shot recognition benchmarks.
Meta-Transfer Learning for Few-Shot Learning
CVPR 2019
• y2l/meta-transfer-learning-tensorflow
•
In this paper we propose a novel few-shot learning method called meta-transfer learning (MTL) which learns to adapt a deep NN for few shot learning tasks.
FEW-SHOT IMAGE CLASSIFICATION FEW-SHOT LEARNING TRANSFER LEARNING