Although deep learning has achieved satisfactory performance in computer vision, a large
volume of images is required. However, collecting images is often expensive and …

Data is the fuel to models, and it is still applicable in fault localization (FL). Many existing
elaborate FL techniques take the code coverage matrix and failure vector as inputs …

Nuclei detection is one of the most fundamental and challenging problems in
histopathological image analysis, which can localize nuclei to provide effective computer …

Unsupervised domain adaptation techniques increase the classification performance of
tasks from the target domain by utilizing the information in a related source domain. Since …

The question of molecular similarity is core in cheminformatics and is usually assessed via a
pairwise comparison based on vectors of properties or molecular fingerprints. We recently …

In the aftermath of GPT-3.5, commonly known as ChatGPT, research have attempted to
assess its capacity for lowering annotation cost, either by doing zero-shot learning …

In reservoir engineering, numerical simulators are crucial for analyzing risks and
uncertainties. The decision-making plan is complex due to numerous uncertain variables …

One of the greatest limitations in the field of EEG-based emotion recognition is the lack of
training samples, which makes it difficult to establish effective models for emotion …

This review article comprehensively delves into the rapidly evolving field of domain
adaptation in computer and robotic vision. It offers a detailed technical analysis of the …

Abstract Domain adaptation aims to alleviate distribution gaps between source and target
domains. However, when the available target domain data are scarce for training, learning …