Simple Control Baselines for Evaluating Transfer Learning
Andrei Atanov*
Shijian Xu*
Onur Beker
Andrei Filatov
Amir Zamir

What are the most informative control baselines and transfer dataset sizes that provide sufficient context for evaluating the effectiveness of a pre-training method? We employ a calibrated evaluation standard that incorporates: 1) a scratch control baseline to account for the architectural inductive biases, 2) a maximal-supervision baseline to approximate the best achievable performance, and 3) a blind-guess baseline to account for the dataset bias.


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, often hindering a nuanced and calibrated understanding of the real efficacy of the methods. We propose 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 proposed 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 others, such as dense pixel-wise predictions.

Code for Evaluation  [GitHub]

Paper and Supplementary Material

Simple Control Baselines for Evaluating Transfer Learning
Andrei Atanov*, Shijian Xu*, Onur Beker, Andrei Filatov, Amir Zamir

arXiv, 2022

[Bibtex] [arXiv Paper] [Supplimentary]


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