Few-Shot Image Classification
215 papers with code • 88 benchmarks • 24 datasets
Few-Shot Image Classification is a computer vision task that involves training machine learning models to classify images into predefined categories using only a few labeled examples of each category (typically < 6 examples). The goal is to enable models to recognize and classify new images with minimal supervision and limited data, without having to train on large datasets. (typically < 6 examples)
( Image credit: Learning Embedding Adaptation for Few-Shot Learning )
Libraries
Use these libraries to find Few-Shot Image Classification models and implementationsDatasets
Subtasks
Most implemented papers
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
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.
Learning Transferable Visual Models From Natural Language Supervision
State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories.
Prototypical Networks for Few-shot Learning
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.
Matching Networks for One Shot Learning
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.
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples
Few-shot classification refers to learning a classifier for new classes given only a few examples.
Learning to Compare: Relation Network for Few-Shot Learning
Once trained, a RN is able to classify images of new classes by computing relation scores between query images and the few examples of each new class without further updating the network.
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.
How to train your MAML
The field of few-shot learning has recently seen substantial advancements.
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
Many machine learning tasks such as multiple instance learning, 3D shape recognition, and few-shot image classification are defined on sets of instances.
ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models
In general, these language-augmented visual models demonstrate strong transferability to a variety of datasets and tasks.