Few-shot learning (FSL) has emerged as an effective learning method and shows great
potential. Despite the recent creative works in tackling FSL tasks, learning valid information …

Deep supervised learning algorithms typically require a large volume of labeled data to
achieve satisfactory performance. However, the process of collecting and labeling such data …

Few-shot learning (FSL) is an important and topical problem in computer vision that has
motivated extensive research into numerous methods spanning from sophisticated meta …

Despite the impressive performance in many individual tasks, deep neural networks suffer
from catastrophic forgetting when learning new tasks incrementally. Recently, various …

We propose to address the problem of few-shot classification by meta-learning" what to
observe" and" where to attend" in a relational perspective. Our method leverages relational …

Deep learning-based models have been shown to outperform human beings in many
computer vision tasks with massive available labeled training data in learning. However …

Given new tasks with very little data---such as new classes in a classification problem or a
domain shift in the input---performance of modern vision systems degrades remarkably …

Spiking neural networks (SNNs) are regarded as a promising candidate to deal with the
major challenges of current machine learning techniques, including the high energy …

Abstract Model-agnostic meta learning (MAML) is currently one of the dominating
approaches for few-shot meta-learning. Albeit its effectiveness, the optimization of MAML …

This paper introduces a negative margin loss to metric learning based few-shot learning
methods. The negative margin loss significantly outperforms regular softmax loss, and …