[PDF] Self-Supervised Learning for Few-Shot Image Classification

Boosting Few-Shot Visual Learning with Self-Supervision - Spyros

Few-shot learning and self-supervised learning address different facets of the same problem: how to train a model with little or no labeled data.

When Does Self-supervision Improve Few-shot Learning?

Self-supervised tasks such as jigsaw puzzle or rotation prediction act as a data-dependent regularizer for the shared feature backbone. Our work investigates 

Self-Supervised Learning for Few-Shot Image Classification

Conditional Self-Supervised Learning for Few-Shot Classification

However learning a good representation by traditional self-supervised methods is usually de- pendent on large training samples. In few-shot sce- narios

MaskSplit: Self-Supervised Meta-Learning for Few-Shot Semantic

alleviate this need we propose a self-supervised training approach for learning few-shot segmentation models. We first use unsupervised saliency estimation 

A Survey of Self-Supervised and Few-Shot Object Detection

27-Oct-2021 in learning self-supervised representations several (few- shot) detection methods now initialize their backbone from.

COVID-19 Surveillance through Twitter using Self-Supervised and

With the seasonal in- fluenza and novel Coronavirus having many similar symptoms we propose using few shot learning to fine-tune a semi-supervised model built 

Improving In-Context Few-Shot Learning via Self-Supervised Training

10-Jul-2022 Self-supervised pretraining has made few-shot learning possible for many NLP tasks. But the pretraining objectives are not typically.

Pareto Self-Supervised Training for Few-Shot Learning

While few-shot learning (FSL) aims for rapid generaliza- tion to new concepts with little supervision self-supervised learning (SSL) constructs supervisory 

SELF-SUPERVISED CONTRASTIVE ZERO TO FEW-SHOT

The current prevailing approach to supervised and few-shot learning is to use datasets for pretraining or just more (broader) self-supervised learning ...