1 citations · 1 across the 6 of their papers we have counts for
7 papers
HEART: Hyperspherical Embedding Alignment via Kent-Representation Traversal in Diffusion Models
Arani Roy, Shristi Das Biswas, Kaushik Roy
Text-to-image diffusion models can generate visually stunning images, yet, controlling what appears and how it appears, remains surprisingly difficult, especially when operating so…
Now You See It, Now You Don't - Instant Concept Erasure for Safe Text-to-Image and Video Generation
Shristi Das Biswas, Arani Roy, Kaushik Roy
Robust concept removal for text-to-image (T2I) and text-to-video (T2V) models is essential for their safe deployment. Existing methods, however, suffer from costly retraining, infe…
Feedback Alignment Meets Low-Rank Manifolds: A Structured Recipe for Local Learning
Arani Roy, Marco P. Apolinario, Shristi Das Biswas +1
Training deep neural networks (DNNs) with backpropagation (BP) achieves state-of-the-art accuracy but requires global error propagation and full parameterization, leading to substa…
SlimDiff: Training-Free, Activation-Guided Hands-free Slimming of Diffusion Models
Arani Roy, Shristi Das Biswas, Kaushik Roy
Diffusion models (DMs), lauded for their generative performance, are computationally prohibitive due to their billion-scale parameters and iterative denoising dynamics. Existing ef…
CURE: Concept Unlearning via Orthogonal Representation Editing in Diffusion Models
Shristi Das Biswas, Arani Roy, Kaushik Roy
As Text-to-Image models continue to evolve, so does the risk of generating unsafe, copyrighted, or privacy-violating content. Existing safety interventions - ranging from training…
Towards Scalable Modeling of Compressed Videos for Efficient Action Recognition
Shristi Das Biswas, Efstathia Soufleri, Arani Roy +1
Training robust deep video representations has proven to be computationally challenging due to substantial decoding overheads, the enormous size of raw video streams, and their inh…
LLS: Local Learning Rule for Deep Neural Networks Inspired by Neural Activity Synchronization
Marco Paul E. Apolinario, Arani Roy, Kaushik Roy
Training deep neural networks (DNNs) using traditional backpropagation (BP) presents challenges in terms of computational complexity and energy consumption, particularly for on-dev…