5 papers
BPG: Balancing Plasticity and Generalization for Domain Incremental Learning
Qiang Wang, Songlin Dong, Shaokun Wang +5
Deep neural networks excel in various tasks but struggle to generalize across evolving data distributions, leading to significant performance degradation under domain shifts. Domai…
Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need
Qiang Wang, Xiang Song, Yuhang He +4
Deep neural networks (DNNs) often underperform in real-world, dynamic settings where data distributions change over time. Domain Incremental Learning (DIL) offers a solution by ena…
DualCP: Rehearsal-Free Domain-Incremental Learning via Dual-Level Concept Prototype
Qiang Wang, Yuhang He, SongLin Dong +4
Domain-Incremental Learning (DIL) enables vision models to adapt to changing conditions in real-world environments while maintaining the knowledge acquired from previous domains. G…
Space Rotation with Basis Transformation for Training-free Test-Time Adaptation
Chenhao Ding, Xinyuan Gao, Songlin Dong +5
With the development of visual-language models (VLM) in downstream task applications, test-time adaptation methods based on VLM have attracted increasing attention for their abilit…
Prompt-Agnostic Adversarial Perturbation for Customized Diffusion Models
Cong Wan, Yuhang He, Xiang Song +1
Diffusion models have revolutionized customized text-to-image generation, allowing for efficient synthesis of photos from personal data with textual descriptions. However, these ad…