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
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 ...
