Integrating knowledge across different domains is an essential feature of human learning.
Learning paradigms such as transfer learning, meta-learning, and multi-task learning reflect …

High penetration of renewable energy leads to dramatic changes in regional load patterns,
forming a duck curve phenomenon that profoundly affects the operation style of the power …

Vision Foundation Models (VFMs) have demonstrated outstanding performance on
numerous downstream tasks. However, due to their inherent representation biases …

Multi-objective optimization (MOO) in deep learning aims to simultaneously optimize
multiple conflicting objectives, a challenge frequently encountered in areas like multi-task …

Preference-Based reinforcement learning (PBRL) learns directly from the preferences of
human teachers regarding agent behaviors without needing meticulously designed reward …

In this work, we reimagine classical probing to evaluate knowledge transfer from simple
source to more complex target tasks. Instead of probing frozen representations from a …

The integration of Federated Learning (FL) and Multi-Task Learning (MTL) has been
explored to address client heterogeneity, with Federated Multi-Task Learning (FMTL) …

Model merging combines knowledge from task-specific models into a unified multi-task
model to avoid joint training on all task data. However, current methods face challenges due …

Deep Neural Networks exhibit inherent vulnerabilities to adversarial attacks, which can
significantly compromise their outputs and reliability. While existing research primarily …

Abstract Copy-Move Forgery Detection (CMFD) is a technique that is designed to identify
image tampering and locate suspicious areas. However, the practicality of the CMFD is …