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Latent Diffusion Unlearning: Protecting Against Unauthorized Personalization Through Trajectory Shifted Perturbations
Naresh Kumar Devulapally, Shruti Agarwal, Tejas Gokhale +1
Text-to-image diffusion models have demonstrated remarkable effectiveness in rapid and high-fidelity personalization, even when provided with only a few user images. However, the e…
Model-Agnostic Gender Bias Control for Text-to-Image Generation via Sparse Autoencoder
Chao Wu, Zhenyi Wang, Kangxian Xie +3
Text-to-image (T2I) diffusion models often exhibit gender bias, particularly by generating stereotypical associations between professions and gendered subjects. This paper presents…
Is Exchangeability better than I.I.D to handle Data Distribution Shifts while Pooling Data for Data-scarce Medical image segmentation?
Ayush Roy, Samin Enam, Jun Xia +2
Data scarcity is a major challenge in medical imaging, particularly for deep learning models. While data pooling (combining datasets from multiple sources) and data addition (addin…
Your Text Encoder Can Be An Object-Level Watermarking Controller
Naresh Kumar Devulapally, Mingzhen Huang, Vishal Asnani +3
Invisible watermarking of AI-generated images can help with copyright protection, enabling detection and identification of AI-generated media. In this work, we present a novel appr…