1 citations · 2 across the 6 of their papers we have counts for
4 papers · 1 filter
Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks
Nobin Sarwar, Shubhashis Roy Dipta, Zheyuan Liu +1
With the growing adoption of VLMs, DMs, LLMs, and AFMs, these multimodal foundation models can inadvertently encode sensitive, copyrighted, biased, or unsafe cross-modal associatio…
Erasing Undesirable Concepts in Diffusion Models with Adversarial Preservation
Anh Bui, Long Vuong, Khanh Doan +4
Diffusion models excel at generating visually striking content from text but can inadvertently produce undesirable or harmful content when trained on unfiltered internet data. A pr…
Hiding and Recovering Knowledge in Text-to-Image Diffusion Models via Learnable Prompts
Anh Bui, Khanh Doan, Trung Le +3
Diffusion models have demonstrated remarkable capability in generating high-quality visual content from textual descriptions. However, since these models are trained on large-scale…
Class-Prototype Conditional Diffusion Model with Gradient Projection for Continual Learning
Khanh Doan, Quyen Tran, Tung Lam Tran +3
Mitigating catastrophic forgetting is a key hurdle in continual learning. Deep Generative Replay (GR) provides techniques focused on generating samples from prior tasks to enhance…