Nov 14, 2019 · Abstract:Few-shot image classification aims to classify unseen classes with limited labelled samples. Recent works benefit from the ...
Few-shot image classification aims to classify unseen classes with limited labelled samples. Recent works benefit from the meta- learning process with episodic ...
In this paper, we proposed to train a more generalized embedding network with self-supervised learning (SSL) which can provide robust representation for ...
Few-Shot Learning & Self Supervised Learning ... Few-shot image classification aims to robustly classify unseen classes with limited samples for each class.
PDF | Few-shot image classification aims to classify unseen classes with limited labeled samples. Recent works benefit from the meta-learning process.
Dec 14, 2022 · Few-Shot Image Classification (FSIC) aims to learn an image classifier with only a few training samples. The key challenge of few-shot image ...
A novel self-supervised contrastive learning for few-shot image classification. •. Contrastive learning is introduced to obtain a better sample discrimination.
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Self-Supervised Learning (SSL): Enhancing Few-Shot Image ...
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Feb 7, 2024 · This research investigates the application of self-supervised learning techniques to enhance few-shot image classification in scenarios with ...
This paper proposed to train a more generalized embedding network with self-supervised learning (SSL) which can provide robust representation for downstream ...
For self- supervised training we used the training images of the base classes of MiniImageNet and for the few-shot classification step we used the test classes ...