4 papers
RAIGen: Rare Attribute Identification in Text-to-Image Generative Models
Silpa Vadakkeeveetil Sreelatha, Dan Wang, Serge Belongie +2
Text-to-image diffusion models achieve impressive generation quality but inherit and amplify training-data biases, skewing coverage of semantic attributes. Prior work addresses thi…
RespoDiff: Dual-Module Bottleneck Transformation for Responsible & Faithful T2I Generation
Silpa Vadakkeeveetil Sreelatha, Sauradip Nag, Muhammad Awais +2
The rapid advancement of diffusion models has enabled high-fidelity and semantically rich text-to-image generation; however, ensuring fairness and safety remains an open challenge.…
DeNetDM: Debiasing by Network Depth Modulation
Silpa Vadakkeeveetil Sreelatha, Adarsh Kappiyath, Abhra Chaudhuri +1
Neural networks trained on biased datasets tend to inadvertently learn spurious correlations, hindering generalization. We formally prove that (1) samples that exhibit spurious cor…
Self-supervised Enhancement of Latent Discovery in GANs
Silpa Vadakkeeveetil Sreelatha, Adarsh Kappiyath, S Sumitra
Several methods for discovering interpretable directions in the latent space of pre-trained GANs have been proposed. Latent semantics discovered by unsupervised methods are relativ…