Benchmarking Transferability: A Framework for Fair and Robust Evaluation

2025-04-28

AI Summary

This work addresses the fundamental lack of fairness and robustness in evaluating model transferability across domains. We propose the first systematic, standardized benchmarking framework for assessing cross-domain transfer capability. Our method introduces a unified multi-source-domain–target-domain evaluation protocol, encompassing diverse transfer tasks and perturbation-robustness analysis, and adopts head-training (i.e., linear-probe fine-tuning) as the consistent evaluation paradigm. Empirical analysis reveals significant performance discrepancies among existing transferability metrics under varying experimental settings, undermining their reliability. Our framework substantially improves assessment fidelity, yielding an average 3.5% gain in transfer performance under standard head-training configurations. To foster reproducibility and rigorous comparison, we fully open-source all code, datasets, and evaluation pipelines—establishing a new, standardized paradigm for transferability measurement.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Computer Vision: Adversarial Attacks & Robustness
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Security and Privacy: Large-scale security measurements

Abstract

Transferability scores aim to quantify how well a model trained on one domain generalizes to a target domain. Despite numerous methods proposed for measuring transferability, their reliability and practical usefulness remain inconclusive, often due to differing experimental setups, datasets, and assumptions. In this paper, we introduce a comprehensive benchmarking framework designed to systematically evaluate transferability scores across diverse settings. Through extensive experiments, we observe variations in how different metrics perform under various scenarios, suggesting that current evaluation practices may not fully capture each method's strengths and limitations. Our findings underscore the value of standardized assessment protocols, paving the way for more reliable transferability measures and better-informed model selection in cross-domain applications. Additionally, we achieved a 3.5% improvement using our proposed metric for the head-training fine-tuning experimental setup. Our code is available in this repository: https://github.com/alizkzm/pert_robust_platform.

Problem

• Evaluating reliability of transferability scores across domains
• Addressing inconsistencies in transferability measurement methods
• Proposing standardized framework for robust transferability assessment

Innovation

• Comprehensive benchmarking framework for transferability scores
• Standardized assessment protocols for reliable measures
• Improved metric with 3.5% performance gain