Simple Control Baselines for Evaluating Transfer Learning

TL;DR

This paper proposes a standardized evaluation framework with simple control baselines for transfer learning, enhancing the interpretability and comparability of transfer performance across different models and tasks.

Contribution

It introduces a set of critical control baselines—blind-guess, scratch-model, and maximal-supervision—for more meaningful transfer learning evaluation.

Findings

Existing self-supervised methods are more effective for image classification than dense predictions.

The proposed evaluation standard clarifies the contribution of architecture and dataset bias.

Using control baselines leads to more nuanced understanding of transfer learning performance.

Abstract

Transfer learning has witnessed remarkable progress in recent years, for example, with the introduction of augmentation-based contrastive self-supervised learning methods. While a number of large-scale empirical studies on the transfer performance of such models have been conducted, there is not yet an agreed-upon set of control baselines, evaluation practices, and metrics to report, which often hinders a nuanced and calibrated understanding of the real efficacy of the methods. We share an evaluation standard that aims to quantify and communicate transfer learning performance in an informative and accessible setup. This is done by baking a number of simple yet critical control baselines in the evaluation method, particularly the blind-guess (quantifying the dataset bias), scratch-model (quantifying the architectural contribution), and maximal-supervision (quantifying the upper-bound). To demonstrate how the evaluation standard can be employed, we provide an example empirical study investigating a few basic questions about self-supervised learning. For example, using this standard, the study shows the effectiveness of existing self-supervised pre-training methods is skewed towards image classification tasks versus dense pixel-wise predictions. In general, we encourage using/reporting the suggested control baselines in evaluating transfer learning in order to gain a more meaningful and informative understanding.