The use of artificial intelligence (AI) is becoming more prevalent across industries such as
healthcare, finance, and transportation. Artificial intelligence is based on the analysis of …

Over the past decade, domain adaptation has become a widely studied branch of transfer
learning which aims to improve performance on target domains by leveraging knowledge …

Unsupervised domain adaptation (UDA) enables the transfer of models trained on source
domains to unlabeled target domains. However, transferring complex time series models …

Artificial intelligence (AI) systems have been shown to help dermatologists diagnose
melanoma more accurately, however they lack transparency, hindering user acceptance …

Abstract Domain Generalization (DG) techniques have emerged as a popular approach to
address the challenges of domain shift in Deep Learning (DL), with the goal of generalizing …

Abstract Neural Radiance Fields (NeRFs) have emerged as promising tools for advancing
autonomous driving (AD) research offering scalable closed-loop simulation and data …

Mechanistic learning refers to the synergistic combination of mechanistic mathematical
modeling and data-driven machine or deep learning. This emerging field finds increasing …

Image-language models with prompt learning have shown remarkable advances in
numerous downstream vision tasks. Nevertheless conventional prompt learning methods …

Nasopharyngeal carcinoma (NPC) is a prevalent and clinically significant malignancy that
predominantly impacts the head and neck area. Precise delineation of the Gross Tumor …

In the field of Transfer Learning, Source-Free Unsupervised Domain Adaptation (SFUDA)
emerges as a practical and novel task that enables a pre-trained model to adapt to a new …