The scaling of Transformers has driven breakthrough capabilities for language models. At
present, the largest large language models (LLMs) contain upwards of 100B parameters …

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 …

In this paper, we look at the problem of cross-domain few-shot classification that aims to
learn a classifier from previously unseen classes and domains with few labeled samples …

Transfer-learning methods aim to improve performance in a data-scarce target domain using
a model pretrained on a data-rich source domain. A cost-efficient strategy, linear probing …

Deep learning has been highly successful in computer vision with large amounts of labeled
data, but struggles with limited labeled training data. To address this, Few-shot learning …

Finding candidate molecules with favorable pharmacological activity, low toxicity, and
proper pharmacokinetic properties is an important task in drug discovery. Deep neural …

Monitoring plant diseases is essential for farmers to secure crop quantity and quality. Deep
learning has recently been applied to plant disease recognition to help farmers take prompt …

A central task in computational drug discovery is to construct models from known active
molecules to find further promising molecules for subsequent screening. However, typically …

Abstract Cross-Domain Few-Shot Learning (CDFSL) aims to classify new categories from
new domains with few samples. It confronts a greater domain shift than Few-Shot Learning …

Most current few-shot learning methods train a model from abundantly labeled base
category data and then transfer and adapt the model to sparsely labeled novel category …