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most citedLatent Diffusion Unlearning: Protecting Against Unauthorized Personalization Through Trajectory Shifted Perturbations

1 citations · 1 across the 15 of their papers we have counts for

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cs.CV2026

Score-Control for Hallucination Reduction in Diffusion Models

Mahesh Bhosale, Naresh Kumar Devulapally, Abdul Wasi +3

Diffusion models have emerged as the backbone of modern generative AI, powering advances in vision, language, audio and other modalities. Despite their success, they suffer from ha…

cs.CV2026

ManifoldGD: Training-Free Hierarchical Manifold Guidance for Diffusion-Based Dataset Distillation

Ayush Roy, Wei-Yang Alex Lee, Rudrasis Chakraborty +1

In recent times, large datasets hinder efficient model training while also containing redundant concepts. Dataset distillation aims to synthesize compact datasets that preserve the…

cs.CV2026

Forget Less by Learning Together through Concept Consolidation

Arjun Ramesh Kaushik, Naresh Kumar Devulapally, Vishnu Suresh Lokhande +2

Custom Diffusion Models (CDMs) have gained significant attention due to their remarkable ability to personalize generative processes. However, existing CDMs suffer from catastrophi…

cs.CV2026

Forget Less by Learning from Parents Through Hierarchical Relationships

Arjun Ramesh Kaushik, Naresh Kumar Devulapally, Vishnu Suresh Lokhande +2

Custom Diffusion Models (CDMs) offer impressive capabilities for personalization in generative modeling, yet they remain vulnerable to catastrophic forgetting when learning new con…

cs.CV2025★ 1 cited

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…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2024

Craft: Cross-modal Aligned Features Improve Robustness of Prompt Tuning

Jingchen Sun, Rohan Sharma, Vishnu Suresh Lokhande +1

Prompt Tuning has emerged as a prominent research paradigm for adapting vision-language models to various downstream tasks. However, recent research indicates that prompt tuning me…

cs.CV2024

ParallelEdits: Efficient Multi-object Image Editing

Mingzhen Huang, Jialing Cai, Shan Jia +2

Text-driven image synthesis has made significant advancements with the development of diffusion models, transforming how visual content is generated from text prompts. Despite thes…