Benchmarking Few-shot Transferability of Pre-trained Models with Improved Evaluation Protocols
arXiv:2603.00478v1 Announce Type: new Abstract: Few-shot transfer has been revolutionized by stronger pre-trained models and improved adaptation algorithms.However, there lacks a unified, rigorous evaluation protocol that is both challenging and realistic for real-world usage. In this work, we establish FEWTRANS, a comprehensive benchmark containing 10 diverse datasets, and propose the Hyperparameter Ensemble (HPE) protocol to overcome the "validation set illusion" in data-scarce regimes. Our empirical findings demonstrate that the choice of pre-trained model is the dominant factor for performance, while many sophisticated transfer methods offer negligible practical advantages over a simple full-parameter fine-tuning baseline. To explain this surprising effectiveness, we provide an in-depth mechanistic analysis showing that full fine-tuning succeeds via distributed micro-adjustments and more flexible reshaping of high-level semantic presentations without suffering from overfitting. Ad
arXiv:2603.00478v1 Announce Type: new Abstract: Few-shot transfer has been revolutionized by stronger pre-trained models and improved adaptation algorithms.However, there lacks a unified, rigorous evaluation protocol that is both challenging and realistic for real-world usage. In this work, we establish FEWTRANS, a comprehensive benchmark containing 10 diverse datasets, and propose the Hyperparameter Ensemble (HPE) protocol to overcome the "validation set illusion" in data-scarce regimes. Our empirical findings demonstrate that the choice of pre-trained model is the dominant factor for performance, while many sophisticated transfer methods offer negligible practical advantages over a simple full-parameter fine-tuning baseline. To explain this surprising effectiveness, we provide an in-depth mechanistic analysis showing that full fine-tuning succeeds via distributed micro-adjustments and more flexible reshaping of high-level semantic presentations without suffering from overfitting. Additionally, we quantify the performance collapse of multimodal models in specialized domains as a result of linguistic rarity using adjusted Zipf frequency scores. By releasing FEWTRANS, we aim to provide a rigorous "ruler" to streamline reproducible advances in few-shot transfer learning research. We make the FEWTRANS benchmark publicly available at https://github.com/Frankluox/FewTrans.
Executive Summary
This article introduces FEWTRANS, a comprehensive benchmark for evaluating few-shot transferability of pre-trained models. The authors propose the Hyperparameter Ensemble protocol to address the 'validation set illusion' and demonstrate that the choice of pre-trained model is the dominant factor for performance. The study finds that full fine-tuning is surprisingly effective and offers negligible practical advantages over sophisticated transfer methods. The FEWTRANS benchmark aims to provide a rigorous evaluation protocol for few-shot transfer learning research.
Key Points
- ▸ Introduction of FEWTRANS, a comprehensive benchmark for few-shot transferability
- ▸ Proposal of the Hyperparameter Ensemble protocol to address the 'validation set illusion'
- ▸ Finding that the choice of pre-trained model is the dominant factor for performance
Merits
Comprehensive Benchmark
FEWTRANS provides a unified and rigorous evaluation protocol for few-shot transfer learning research
Improved Evaluation Protocol
The Hyperparameter Ensemble protocol helps to overcome the 'validation set illusion' in data-scarce regimes
Demerits
Limited Exploration of Transfer Methods
The study finds that many sophisticated transfer methods offer negligible practical advantages over a simple full-parameter fine-tuning baseline
Expert Commentary
The introduction of FEWTRANS and the Hyperparameter Ensemble protocol represents a significant contribution to the field of few-shot transfer learning. The study's findings on the effectiveness of full fine-tuning and the limited advantages of sophisticated transfer methods are surprising and warrant further investigation. The FEWTRANS benchmark has the potential to become a widely-adopted evaluation protocol, enabling more rigorous and reproducible research in few-shot transfer learning. However, further exploration of transfer methods and their limitations is necessary to fully realize the potential of few-shot learning.
Recommendations
- ✓ Future research should explore the limitations of transfer methods and their potential applications in real-world scenarios
- ✓ The development of more efficient and effective few-shot transfer learning methods should be prioritized, with a focus on addressing the challenges of overfitting and data scarcity