Home Research Papers Datasets State of the Art Pricing

Discover, visualize, and connect AI research papers. Explore the latest trends and insights in artificial intelligence research.

Product

Home
Research Papers
About

Support

Contact
Terms of Service
Privacy Policy

© 2026 Papersgraph. All rights reserved.

few-shot-image-classification

Unsupervised Few-Shot Image Classification

3260 papers • 126 benchmarks • 313 datasets

In contrast to (supervised) few-shot image classification, only the unlabeled dataset is available in the pre-training or meta-training stage for unsupervised few-shot image classification.

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in few-shot-image-classification

Trend

Dataset

Best Model

Actions

Mini-Imagenet 5-way (1-shot)

Mini-Imagenet 5-way (1-shot)

Mini-Imagenet 5-way (5-shot)

Mini-Imagenet 5-way (5-shot)

Libraries

i

Use these libraries to find few-shot-image-classification models and implementations

Datasets

mini-Imagenet

tieredImageNet

Subtasks

No subtasks available.

Most implemented papers

Self-Supervision Can Be a Good Few-Shot Learner

Xinmei Tian, Jianzhuang Liu, Yuning Lu, Liangjiang Wen, Yajing Liu•Mon Jul 18 2022

This work proposes an effective unsupervised FSL method, learning representations with self-supervision, following the InfoMax principle, which achieves comparable performance on widely used FSL benchmarks without any labels of the base classes.

51

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

Unsupervised Few-Shot Image Classification | State-of-the-Art

Tiered ImageNet 5-way (1-shot)

Tiered ImageNet 5-way (1-shot)

Tiered ImageNet 5-way (5-shot)

Tiered ImageNet 5-way (5-shot)

0

Self-Supervised Learning for Few-Shot Image Classification

Hui Xue, Da Chen, YueFeng Chen, Yuhong Li, Feng Mao, Yuan He•Wed Nov 13 2019

This paper proposed to train a more generalized embedding network with self-supervised learning (SSL) which can provide robust representation for downstream tasks by learning from the data itself.

128 0

Self-Supervised Prototypical Transfer Learning for Few-Shot Classification

Carlos Medina, A. Devos, M. Grossglauser•Thu Jun 18 2020

It is demonstrated that the self-supervised prototypical transfer learning approach ProtoTransfer outperforms state-of-the-art unsupervised meta-learning methods on few-shot tasks from the mini-ImageNet dataset and has comparable performance to supervised methods, but requires orders of magnitude fewer labels.

57 0

Diversity Helps: Unsupervised Few-shot Learning via Distribution Shift-based Data Augmentation

Wenbin Li, Yinghuan Shi, Yang Gao, Tiexin Qin•Sun Apr 12 2020

This paper develops a novel framework called Unsupervised Few-shot Learning via Distribution Shift-based Data Augmentation (ULDA), which pays attention to the distribution diversity inside each constructed pretext few-shot task when using data augmentation.

20 0

Rethinking Class Relations: Absolute-relative Supervised and Unsupervised Few-shot Learning

Philip H. S. Torr, Piotr Koniusz, Hongguang Zhang, Songlei Jian, Hongdong Li•Sat Jan 11 2020

This work rethink the relations between class concepts, and proposes a novel Absolute-relative Learning paradigm to fully take advantage of label information to refine the image an relation representations in both supervised and unsupervised scenarios.

71 0

Self-Supervised Class-Cognizant Few-Shot Classification

O. Shirekar, H. J. Rad•Mon Feb 14 2022

This paper focuses on unsupervised learning from an abundance of unlabeled data followed by few-shot fine-tuning on a downstream classification task and extends a recent study on adopting contrastive learning for self-supervised pre-training by incorporating class-level cognizance through iterative clustering and re-ranking and expanding the contrastive optimization loss to account for it.

6 0

Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning

Jianbing Shen, Ling Shao, Xingping Dong•Mon Sep 26 2022

This work addresses the core reason for lack of a clustering-friendly property in the embedding space by minimizing the inter- to intra-class similarity ratio to provide clustered-friendly embedding features, and validates the approach through comprehensive experiments.

16 0

Multi-Level Second-Order Few-Shot Learning

Hongdong Li, Hongguang Zhang, Piotr Koniusz•Fri Jan 14 2022

A Multi-level Second-order (MlSo) few-shots learning network for supervised or unsupervised few-shot image classification and few- shot action recognition is proposed, leveraging so-called power-normalized second-order base learner streams combined with features that express multiple levels of visual abstraction, and self-supervised discriminating mechanisms are used.

39 0

Revisiting Unsupervised Meta-Learning via the Characteristics of Few-Shot Tasks

Han-Jia Ye, De-chuan Zhan, Lu Han•Sun Nov 29 2020

This work removes the requirement of base class labels and learns generalizable embeddings via Unsupervised Meta-Learning (UML), and applies embedding-based classifiers to novel tasks with labeled few-shot examples during meta-test.

37 0

Adding a benchmark result helps the community track progress.