Currently, few samples and the inevitable noise poses a severe test on deep learning
methods. To solve the above problems, a novel fault diagnosis network based on a refined …

New classes arise frequently in our ever-changing world, eg, emerging topics in social
media and new types of products in e-commerce. A model should recognize new classes …

The goal of few-shot learning is to learn the generalizability from seen to unseen data with
only a few samples. Most previous few-shot learning focus on learning generalizability …

Calibration-based methods have dominated RAW image denoising under extremely low-
light environments. However, these methods suffer from several main deficiencies: 1) the …

As for few-shot image classification, recently, some works revisit the standard transfer
learning paradigm, ie, pre-training and fine-tuning, and have achieved some success …

Few-shot remote sensing scene classification (RSSC) is of high practical value in real
situations where data are scarce and annotated costly. The few-shot learner needs to …

Model-agnostic meta-learning (MAML) is arguably one of the most popular meta-learning
algorithms nowadays. Nevertheless, its performance on few-shot classification is far behind …

Data-free meta-learning (DFML) aims to enable efficient learning of new tasks by meta-
learning from a collection of pre-trained models without access to the training data. Existing …

Few-shot action recognition is a challenging but practical problem aiming to learn a model
that can be easily adapted to identify new action categories with only a few labeled samples …

Few-shot learning (FSL) aims to recognize new concepts using a limited number of visual
samples. Existing approaches attempt to incorporate semantic information into the limited …