# Self-Supervised Learning For Few-Shot Image Classification

arXiv: https://arxiv.org/abs/1911.06045 · HTML: https://mlsanity.com/paper/1911.06045/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2019.

## Built on by (most cited first, 30 of 44)

- [A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2205.06743/) (2022)
- [Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference](https://mlsanity.com/paper/2204.07305/) (2022)
- [COCO-O: A Benchmark for Object Detectors under Natural Distribution Shifts](https://mlsanity.com/paper/2307.12730/) (2023)
- [ORBIT: A Real-World Few-Shot Dataset for Teachable Object Recognition](https://mlsanity.com/paper/2104.03841/) (2021)
- [Wave-SAN: Wavelet based Style Augmentation Network for Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2203.07656/) (2022)
- [Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction](https://mlsanity.com/paper/2205.02708/) (2022)
- [Context-Aware Meta-Learning](https://mlsanity.com/paper/2310.10971/) (2023)
- [Stealthy Backdoor Attack in Self-Supervised Learning Vision Encoders for Large Vision Language Models](https://mlsanity.com/paper/2502.18290/) (2025)
- [CAD: Co-Adapting Discriminative Features for Improved Few-Shot Classification](https://mlsanity.com/paper/2203.13465/) (2022)
- [Discriminative Sample-Guided and Parameter-Efficient Feature Space Adaptation for Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2403.04492/) (2024)
- [A Survey of Deep Learning for Low-Shot Object Detection](https://mlsanity.com/paper/2112.02814/) (2021)
- [On the generalization capabilities of FSL methods through domain adaptation: a case study in endoscopic kidney stone image classification](https://mlsanity.com/paper/2205.00895/) (2022)
- [Automated Human Cell Classification in Sparse Datasets using Few-Shot Learning](https://mlsanity.com/paper/2107.13093/) (2021)
- [Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning](https://mlsanity.com/paper/2201.05151/) (2022)
- [How Well Do Self-Supervised Methods Perform in Cross-Domain Few-Shot Learning?](https://mlsanity.com/paper/2202.09014/) (2022)
- [Adaptive manifold for imbalanced transductive few-shot learning](https://mlsanity.com/paper/2304.14281/) (2023)
- [Domain Adaptive Few-Shot Open-Set Learning](https://mlsanity.com/paper/2309.12814/) (2023)
- [LSTTN: A Long-Short Term Transformer-based Spatio-temporal Neural Network for Traffic Flow Forecasting](https://mlsanity.com/paper/2403.16495/) (2024)
- [Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting](https://mlsanity.com/paper/2507.20834/) (2025)
- [Modular Adaptation for Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2104.00619/) (2021)
- [Dynamic Sub-Cluster-Aware Network for Few-Shot Skin Disease Classification](https://mlsanity.com/paper/2207.01072/) (2022)
- [Self-Supervised Class-Cognizant Few-Shot Classification](https://mlsanity.com/paper/2202.08149/) (2022)
- [Finding Significant Features for Few-Shot Learning using Dimensionality Reduction](https://mlsanity.com/paper/2107.06992/) (2021)
- [Fine-grained Hand Gesture Recognition in Multi-viewpoint Hand Hygiene](https://mlsanity.com/paper/2109.02917/) (2021)
- [Domain Agnostic Few-Shot Learning For Document Intelligence](https://mlsanity.com/paper/2111.00007/) (2021)
- [Partitioning Image Representation in Contrastive Learning](https://mlsanity.com/paper/2203.10454/) (2022)
- [SuSana Distancia is all you need: Enforcing class separability in metric learning via two novel distance-based loss functions for few-shot image classification](https://mlsanity.com/paper/2305.09062/) (2023)
- [Simple but Effective Unsupervised Classification for Specified Domain Images: A Case Study on Fungi Images](https://mlsanity.com/paper/2311.08995/) (2023)
- [Boosting Few-Shot Learning with Disentangled Self-Supervised Learning and Meta-Learning for Medical Image Classification](https://mlsanity.com/paper/2403.17530/) (2024)
- [Tiny models from tiny data: Textual and null-text inversion for few-shot distillation](https://mlsanity.com/paper/2406.03146/) (2024)

## Abstract (arXiv)

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.