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CRAFT: Curriculum Rank Adversarial Fine-Tuning for Robust Vision Language Models
In this work, we propose CRAFT, a unified low-rank fine-tuning framework designed to enhance both out-of-distribution (OOD) and adversarial robustness by integrating adaptive adversarial weight perturbations into a curriculum-driven Low-Rank Adaptation (LoRA) framework.
Shivang Chopra, Chengyue Huang, Brisa Maneechotesuwan, Zsolt Kira.
CRAFT: Curriculum Rank Adversarial Fine-Tuning for Robust Vision Language Models
Source-Free Domain Adaptation with Diffusion-Guided Source Data Generation
This paper introduces a novel approach to leverage the generalizability capability of Diffusion Models for Source-Free Domain Adaptation (DM-SFDA).
Shivang Chopra, Suraj Kothawad, Houda Aynaou, Aman Chadha
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Source-Free Domain Adaptation with Diffusion-Guided Source Data Generation