MoESD: Mixture of Experts Stable Diffusion to Mitigate Gender Bias
Text-to-image models are known to propagate social biases. For example, when prompted to generate images of people in certain professions, these models tend to systematically generate specific genders or ethnicities. In this paper, we show that this bias is already present in the text encoder of the model and introduce a Mixture-of-Experts approach by identifying text-encoded bias in the latent space and then creating a Bias-Identification Gate mechanism. More specifically, we propose MoESD (Mixture of Experts Stable Diffusion) with BiAs (Bias Adapters) to mitigate gender bias in text-to-image models. We also demonstrate that introducing an arbitrary special token to the prompt is essential during the mitigation process. With experiments focusing on gender bias, we show that our approach successfully mitigates gender bias while maintaining image quality.
Code (0)
등록된 구현이 없습니다.
Tasks
Mixture-of-ExpertsSimilar Papers 제목 키워드 기반
Tempo as the Stable Cue: Hierarchical Mixture of Tempo and Beat Experts for Music to 3D Dance Generation
Music to 3D dance generation aims to synthesize realistic and rhythmically synchronized human dance from music. While existing methods often rely on additional genre labels to further improve dance generation, such label…
Abstracting Robot Manipulation Skills via Mixture-of-Experts Diffusion Policies
Diffusion-based policies have recently shown strong results in robot manipulation, but their extension to multi-task scenarios is hindered by the high cost of scaling model size and demonstrations. We introduce Skill Mix…
Multi-Task LearningRobot ManipulationTransfer LearningMixture of Efficient Diffusion Experts Through Automatic Interval and Sub-Network Selection
Diffusion probabilistic models can generate high-quality samples. Yet, their sampling process requires numerous denoising steps, making it slow and computationally intensive. We propose to reduce the sampling cost by pru…
DenoisingTowards Stable and Effective Reinforcement Learning for Mixture-of-Experts
Recent advances in reinforcement learning (RL) have substantially improved the training of large-scale language models, leading to significant gains in generation quality and reasoning ability. However, most existing res…
Reinforcement LearningLanguage-Conditioned Representations and Mixture-of-Experts Policy for Robust Multi-Task Robotic Manipulation
Perceptual ambiguity and task conflict limit multitask robotic manipulation via imitation learning. We propose a framework combining a Language-Conditioned Visual Representation (LCVR) module and a Language-conditioned M…