Cover Image

Rethinking Weight Decay for Robust Fine-Tuning of Foundation Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Tian, Junjiao, Huang, Chengyue, Kira, Zsolt
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Computation and Language Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Directional Gradient Projection for Robust Fine-Tuning of Foundation Models
by: Huang, Chengyue, et al.
Published: (2025)

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering
by: Huang, Chengyue, et al.
Published: (2025)

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models
by: Huang, Chengyue, et al.
Published: (2025)

Continual Adaptation of Vision Transformers for Federated Learning
by: Halbe, Shaunak, et al.
Published: (2023)

MAPS: Preserving Vision-Language Representations via Module-Wise Proximity Scheduling for Better Vision-Language-Action Generalization
by: Huang, Chengyue, et al.
Published: (2025)

Recovering the Pre-Fine-Tuning Weights of Generative Models
by: Horwitz, Eliahu, et al.
Published: (2024)

Grounding Descriptions in Images informs Zero-Shot Visual Recognition
by: Halbe, Shaunak, et al.
Published: (2024)

Adaptive Memory Replay for Continual Learning
by: Smith, James Seale, et al.
Published: (2024)

The Geometry of Robustness: Optimizing Loss Landscape Curvature and Feature Manifold Alignment for Robust Finetuning of Vision-Language Models
by: Chopra, Shivang, et al.
Published: (2026)

Rethinking Fine-Tuning: Unlocking Hidden Capabilities in Vision-Language Models
by: Zhang, Mingyuan, et al.
Published: (2025)

Bridging the Visual Gap: Fine-Tuning Multimodal Models with Knowledge-Adapted Captions
by: Yanuka, Moran, et al.
Published: (2024)

Sim-CLIP: Unsupervised Siamese Adversarial Fine-Tuning for Robust and Semantically-Rich Vision-Language Models
by: Hossain, Md Zarif, et al.
Published: (2024)

Memory-Space Visual Prompting for Efficient Vision-Language Fine-Tuning
by: Jie, Shibo, et al.
Published: (2024)

Reassessing the Role of Supervised Fine-Tuning: An Empirical Study in VLM Reasoning
by: Yu, Yongcan, et al.
Published: (2025)

Multilingual OCR-Aware Fine-Tuning and Prompt-Guided Chain-of-Thought Reasoning for Multimodal Large Language Models
by: Xu, Qinwu, et al.
Published: (2026)

Task-Specific Directions: Definition, Exploration, and Utilization in Parameter Efficient Fine-Tuning
by: Si, Chongjie, et al.
Published: (2024)

Diffuse, Attend, and Segment: Unsupervised Zero-Shot Segmentation using Stable Diffusion
by: Tian, Junjiao, et al.
Published: (2023)

Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method
by: Pan, Bikang, et al.
Published: (2024)

Differentially Private Bias-Term Fine-tuning of Foundation Models
by: Bu, Zhiqi, et al.
Published: (2022)

On the Robustness Tradeoff in Fine-Tuning
by: Li, Kunyang, et al.
Published: (2025)

Tune without Validation: Searching for Learning Rate and Weight Decay on Training Sets
by: Brigato, Lorenzo, et al.
Published: (2024)

Self-Play Fine-Tuning of Diffusion Models for Text-to-Image Generation
by: Yuan, Huizhuo, et al.
Published: (2024)

Pre-trained Text-to-Image Diffusion Models Are Versatile Representation Learners for Control
by: Gupta, Gunshi, et al.
Published: (2024)

Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts
by: Shi, Jiang-Xin, et al.
Published: (2023)

Intern-S1: A Scientific Multimodal Foundation Model
by: Bai, Lei, et al.
Published: (2025)

Rethinking Misalignment in Vision-Language Model Adaptation from a Causal Perspective
by: Zhang, Yanan, et al.
Published: (2024)

Efficient and Versatile Robust Fine-Tuning of Zero-shot Models
by: Kim, Sungyeon, et al.
Published: (2024)

Fine-Tuning Small Language Models for Domain-Specific AI: An Edge AI Perspective
by: Aralimatti, Rakshit, et al.
Published: (2025)

Semi-Supervised Fine-Tuning of Vision Foundation Models with Content-Style Decomposition
by: Drozdova, Mariia, et al.
Published: (2024)

Exploring Parameter-Efficient Fine-Tuning to Enable Foundation Models in Federated Learning
by: Sun, Guangyu, et al.
Published: (2022)

Multi-Task Model Merging via Adaptive Weight Disentanglement
by: Xiong, Feng, et al.
Published: (2024)

Imperfect Vision Encoders: Efficient and Robust Tuning for Vision-Language Models
by: Panos, Aristeidis, et al.
Published: (2024)

Rethinking Weight-Averaged Model-merging
by: Wang, Hu, et al.
Published: (2024)

Intern-S1-Pro: Scientific Multimodal Foundation Model at Trillion Scale
by: Zou, Yicheng, et al.
Published: (2026)

Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learning
by: Zhai, Yuexiang, et al.
Published: (2024)

ElaLoRA: Elastic & Learnable Low-Rank Adaptation for Efficient Model Fine-Tuning
by: Chang, Huandong, et al.
Published: (2025)

Domain-Aware Fine-Tuning of Foundation Models
by: Kaplan, Ugur Ali, et al.
Published: (2024)

BioCLIP: A Vision Foundation Model for the Tree of Life
by: Stevens, Samuel, et al.
Published: (2023)

MambaPEFT: Exploring Parameter-Efficient Fine-Tuning for Mamba
by: Yoshimura, Masakazu, et al.
Published: (2024)

Benchmarking Zero-Shot Robustness of Multimodal Foundation Models: A Pilot Study
by: Wang, Chenguang, et al.
Published: (2024)