Large-scale text-to-image diffusion models can generate high-fidelity images with powerful
compositional ability. However, these models are typically trained on an enormous amount …

Despite the ability to train capable LLMs, the methodology for maintaining their relevancy
and rectifying errors remains elusive. To this end, the past few years have witnessed a surge …

The significant advances in applications of text-to-image generation models have prompted
the demand of a post-hoc adaptation algorithms that can efficiently remove unwanted …

We explore machine unlearning (MU) in the domain of large language models (LLMs),
referred to as LLM unlearning. This initiative aims to eliminate undesirable data influence …

Text-to-image models suffer from various safety issues that may limit their suitability for
deployment. Previous methods have separately addressed individual issues of bias …

Recent developments in multimodal training methodologies, including CLIP and ALIGN,
obviate the necessity for individual data labeling. These approaches utilize pairs of data and …

With evolving data regulations, machine unlearning (MU) has become an important tool for
fostering trust and safety in today's AI models. However, existing MU methods focusing on …

Large-scale text-to-image generative models have been a ground-breaking development in
generative AI, with diffusion models showing their astounding ability to synthesize …

The recent proliferation of large-scale text-to-image models has led to growing concerns that
such models may be misused to generate harmful, misleading, and inappropriate content …

In response to recent data regulation requirements, machine unlearning (MU) has emerged
as a critical process to remove the influence of specific examples from a given model …