Despite its astounding success in learning deeper multi-dimensional data, the performance
of deep learning declines on new unseen tasks mainly due to its focus on same-distribution …

The rapid development of deep learning provides great convenience for production and life.
However, the massive labels required for training models limits further development. Few …

Few-shot classification aims to learn a classifier to recognize unseen classes during training
with limited labeled examples. While significant progress has been made, the growing …

Few-shot classification aims to recognize novel categories with only few labeled images in
each class. Existing metric-based few-shot classification algorithms predict categories by …

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 …

The recent flourish of deep learning in various tasks is largely accredited to the rich and
accessible labeled data. Nonetheless, massive supervision remains a luxury for many real …

Model Agnostic Meta-Learning (MAML) is one of the most representative of gradient-based
meta-learning algorithms. MAML learns new tasks with a few data samples using inner …

The goal of few-shot learning is to learn a classifier that can recognize unseen classes from
limited support data with labels. A common practice for this task is to train a model on the …

Few-shot classification is challenging because the data distribution of the training set can be
widely different to the test set as their classes are disjoint. This distribution shift often results …

Computer vision tasks such as image classification, image retrieval, and few-shot learning
are currently dominated by Euclidean and spherical embeddings so that the final decisions …