Self-Supervised Learning For Few-Shot Image Classification

2019/11/14 by Da Chen, Yuefeng Chen, Chen, Da +9 · 26 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · #Artificial intelligence #Artificial neural network #Bottleneck #Cancer-related molecular mechanisms research #Class (philosophy) #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Domain Adaptation and Few-Shot Learning #Embedding #FOS: Computer and information sciences #Feature learning #Machine learning #Multimodal Machine Learning Applications #Pattern recognition (psychology) #Robustness (evolution) #Supervised learning #Task (project management) #cs.CV

paper · pdf · doi:10.48550/arxiv.1911.06045

published in arXiv (Cornell University) (Cornell University) · To appear at ICASSP 2021. https://github.com/phecy/ssl-few-shot

openalex publication_date 2019/11/14 · arxiv created 2021/02/23 · arxiv updated 2021/02/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

Few-shot image classification aims to classify unseen classes with limited labelled samples. Recent works benefit from the meta-learning process with episodic tasks and can fast adapt to class from training to testing. Due to the limited number of samples for each task, the initial embedding network for meta-learning becomes an essential component and can largely affect the performance in practice. To this end, most of the existing methods highly rely on the efficient embedding network. Due to the limited labelled data, the scale of embedding network is constrained under a supervised learning(SL) manner which becomes a bottleneck of the few-shot learning methods. In this paper, we 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. We evaluate our work by extensive comparisons with previous baseline methods on two few-shot classification datasets (\em i.e., MiniImageNet and CUB) and achieve better performance over baselines. Tests on four datasets in cross-domain few-shot learning classification show that the proposed method achieves state-of-the-art results and further prove the robustness of the proposed model. Our code is available at \hyperref[https://github.com/phecy/SSL-FEW-SHOT.]https://github.com/phecy/SSL-FEW-SHOT.

Citations

• Wide Residual Networks
• Prototypical Networks for Few-shot Learning
• Few-Shot Learning with Graph Neural Networks
• Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)
• Data-Efficient Image Recognition with Contrastive Predictive Coding
• Learning Representations by Maximizing Mutual Information Across Views
• Contrastive Multiview Coding
• Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?

Cited by

• SCS-SupCon: Sigmoid-based Common and Style Supervised Contrastive Learning with Adaptive Decision Boundaries
• Data-Efficient Point Cloud Semantic Segmentation Pipeline for Unimproved Roads
• Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting
• A Comprehensive Survey of Challenges and Opportunities of Few-Shot Learning Across Multiple Domains
• Modular Adaptation for Cross-Domain Few-Shot Learning

Related

• Few-Shot Image Classification via Contrastive Self-Supervised Learning
• Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?
• Learning to Self-Train for Semi-Supervised Few-Shot Classification
• Self-Supervised Prototypical Transfer Learning for Few-Shot Classification
• Few-shot Image Classification: Just Use a Library of Pre-trained Feature Extractors and a Simple Classifier