55 citations · 65 across the 16 of their papers we have counts for
6 papers · 1 filter
MMP: Towards Robust Multi-Modal Learning with Masked Modality Projection
Niki Nezakati, Md Kaykobad Reza, Ameya Patil +2
Multimodal learning seeks to combine data from multiple input sources to enhance the performance of different downstream tasks. In real-world scenarios, performance can degrade sub…
Gaussian is All You Need: A Unified Framework for Solving Inverse Problems via Diffusion Posterior Sampling
Nebiyou Yismaw, Ulugbek S. Kamilov, M. Salman Asif
Diffusion models can generate a variety of high-quality images by modeling complex data distributions. Trained diffusion models can also be very effective image priors for solving…
Targeted Unlearning with Single Layer Unlearning Gradient
Zikui Cai, Yaoteng Tan, M. Salman Asif
Machine unlearning methods aim to remove sensitive or unwanted content from trained models, but typically demand extensive model updates at significant computational cost while pot…
Transform-Dependent Adversarial Attacks
Yaoteng Tan, Zikui Cai, M. Salman Asif
Deep networks are highly vulnerable to adversarial attacks, yet conventional attack methods utilize static adversarial perturbations that induce fixed mispredictions. In this work,…
Cross-Modal Safety Alignment: Is textual unlearning all you need?
Trishna Chakraborty, Erfan Shayegani, Zikui Cai +5
Recent studies reveal that integrating new modalities into Large Language Models (LLMs), such as Vision-Language Models (VLMs), creates a new attack surface that bypasses existing…
Overcoming Distribution Shifts in Plug-and-Play Methods with Test-Time Training
Edward P. Chandler, Shirin Shoushtari, Jiaming Liu +2
Plug-and-Play Priors (PnP) is a well-known class of methods for solving inverse problems in computational imaging. PnP methods combine physical forward models with learned prior mo…