Image de couverture de livre

Preserving Domain Generalization in Fine-Tuning via Joint Parameter Selection

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Pan, Bin, Shen, Shiyu, Wang, Zongbin, Shi, Zhenwei, Xu, Xia
Format:	Preprint
Publié:	2025
Sujets:	Computer Vision and Pattern Recognition Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Efficient Fine-Tuning with Domain Adaptation for Privacy-Preserving Vision Transformer
par: Nagamori, Teru, et autres
Publié: (2024)

Kernelized Sparse Fine-Tuning with Bi-level Parameter Competition for Vision Models
par: Shen, Shufan, et autres
Publié: (2025)

GD-FPS: Growth-Driven Feedforward Parameter Selection for Efficient Fine-Tuning
par: Yang, Kenneth, et autres
Publié: (2025)

Domain Generalization Guided by Large-Scale Pre-Trained Priors
par: Wang, Zongbin, et autres
Publié: (2024)

Task-Specific Directions: Definition, Exploration, and Utilization in Parameter Efficient Fine-Tuning
par: Si, Chongjie, et autres
Publié: (2024)

Spectrum-Aware Parameter Efficient Fine-Tuning for Diffusion Models
par: Zhang, Xinxi, et autres
Publié: (2024)

Domain Agnostic Conditional Invariant Predictions for Domain Generalization
par: Wang, Zongbin, et autres
Publié: (2024)

Mitigating Parameter Interference in Model Merging via Sharpness-Aware Fine-Tuning
par: Lee, Yeoreum, et autres
Publié: (2025)

LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning
par: Zhang, Mingyang, et autres
Publié: (2023)

Federated Joint Learning for Domain and Class Generalization
par: Xu, Haoran, et autres
Publié: (2026)

Selecting Fine-Tuning Examples by Quizzing VLMs
par: Ji, Tenghao, et autres
Publié: (2025)

Parameter-Efficient Fine-Tuning for Pre-Trained Vision Models: A Survey and Benchmark
par: Xin, Yi, et autres
Publié: (2024)

Exploring Parameter-Efficient Fine-Tuning to Enable Foundation Models in Federated Learning
par: Sun, Guangyu, et autres
Publié: (2022)

Taming Noise-Induced Prototype Degradation for Privacy-Preserving Personalized Federated Fine-Tuning
par: Wang, Yuhua, et autres
Publié: (2026)

Domain-Aware Fine-Tuning: Enhancing Neural Network Adaptability
par: Ha, Seokhyeon, et autres
Publié: (2023)

Point-PEFT: Parameter-Efficient Fine-Tuning for 3D Pre-trained Models
par: Tang, Yiwen, et autres
Publié: (2023)

Hyperspectral Image Generation with Unmixing Guided Diffusion Model
par: Shen, Shiyu, et autres
Publié: (2025)

Preserving Silent Features for Domain Generalization
par: Zhao, Chujie, et autres
Publié: (2024)

DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning
par: Shi, Zhengxiang, et autres
Publié: (2023)

Parameter-Efficient Fine-Tuning of DINOv2 for Large-Scale Font Classification
par: Chen, Daniel, et autres
Publié: (2026)

Trustworthy Personalized Bayesian Federated Learning via Posterior Fine-Tune
par: Luo, Mengen, et autres
Publié: (2024)

Learning Semantic Proxies from Visual Prompts for Parameter-Efficient Fine-Tuning in Deep Metric Learning
par: Ren, Li, et autres
Publié: (2024)

Meta Curvature-Aware Minimization for Domain Generalization
par: Chen, Ziyang, et autres
Publié: (2024)

Balancing Multimodal Domain Generalization via Gradient Modulation and Projection
par: Li, Hongzhao, et autres
Publié: (2026)

C$^3$DG: Conditional Domain Generalization for Hyperspectral Imagery Classification with Convergence and Constrained-risk Theories
par: Gao, Zhe, et autres
Publié: (2024)

When Is Rank-1 Enough? Geometry-Guided Initialization for Parameter-Efficient Fine-Tuning
par: Zhao, Haoran, et autres
Publié: (2026)

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation
par: Ly, Son Thai, et autres
Publié: (2024)

AEGIS: Anchor-Enforced Gradient Isolation for Knowledge-Preserving Vision-Language-Action Fine-Tuning
par: Singh, Guransh
Publié: (2026)

LIFT+: Lightweight Fine-Tuning for Long-Tail Learning
par: Shi, Jiang-Xin, et autres
Publié: (2025)

Unlearning Concepts in Diffusion Model via Concept Domain Correction and Concept Preserving Gradient
par: Wu, Yongliang, et autres
Publié: (2024)

CGL: Advancing Continual GUI Learning via Reinforcement Fine-Tuning
par: Yao, Zhenquan, et autres
Publié: (2026)

Domain-Aware Fine-Tuning of Foundation Models
par: Kaplan, Ugur Ali, et autres
Publié: (2024)

Federated Adaptive Prompt Tuning for Multi-Domain Collaborative Learning
par: Su, Shangchao, et autres
Publié: (2022)

AIM-Fair: Advancing Algorithmic Fairness via Selectively Fine-Tuning Biased Models with Contextual Synthetic Data
par: Zhao, Zengqun, et autres
Publié: (2025)

SAM-PTx: Text-Guided Fine-Tuning of SAM with Parameter-Efficient, Parallel-Text Adapters
par: Jalilian, Shayan, et autres
Publié: (2025)

Robust and Generalizable GNN Fine-Tuning via Uncertainty-aware Adapter Learning
par: Jiang, Bo, et autres
Publié: (2025)

GRPO-RM: Fine-Tuning Representation Models via GRPO-Driven Reinforcement Learning
par: Xu, Yanchen, et autres
Publié: (2025)

Data-Free Quantization via Mixed-Precision Compensation without Fine-Tuning
par: Chen, Jun, et autres
Publié: (2023)

BiGain: Unified Token Compression for Joint Generation and Classification
par: Liu, Jiacheng, et autres
Publié: (2026)

LoFT: Parameter-Efficient Fine-Tuning for Long-tailed Semi-Supervised Learning in Open-World Scenarios
par: Huang, Zhiyuan, et autres
Publié: (2025)