Evaluating the Quality of Learned Representations Without Explicit Task Evaluations

In this article, we explore the challenge of evaluating image representations without relying on downstream tasks. The authors argue that simply using a reconstruction loss may not be sufficient to evaluate the quality of learned representations, as it can be easily manipulated and may not reflect the actual metric of interest. They propose a statistical estimator capable of quantifying this quality without depending on downstream evaluations.
The article starts by defining the problem of evaluating image representations in the absence of explicit downstream task evaluations. The authors explain that a fundamental question arises regarding whether we can effectively evaluate the quality of learned representations without relying on downstream tasks. To address this inquiry, they propose exploring statistical estimators capable of quantifying this quality without depending on downstream evaluations.
The article then delves into the theoretical implications of such an approach and its practical value in selecting the best SSL algorithms. The authors highlight the importance of defining "quality" in the context of representations and the need to explore statistical estimators that can accurately quantify this quality without relying on downstream evaluations.
To illustrate their point, the authors cite several examples, including a simple framework for contrastive learning of visual representations (Chen et al., 2020) and exploring simple siamese representation learning (Chen & He, 2021). They show that even though these methods demonstrate impressive results, additional gains can be had by incorporating an input reconstruction term, which helps the learned representation to transfer better to downstream tasks.
However, the authors also acknowledge that extended training durations can significantly deteriorate representation quality, even when employing a set of hyperparameters with proven performance. Therefore, finding an appropriate early stopping criterion is critical.
In conclusion, the article provides a concise summary of the challenges involved in evaluating image representations without relying on downstream tasks and proposes a solution by exploring statistical estimators capable of quantifying representation quality without depending on explicit evaluations. The authors emphasize the importance of defining "quality" in the context of representations and highlight the practical value of selecting the best SSL algorithms using this approach.