Masked image modeling (MIM) has been recognized as a strong and popular self-supervised pre-training approach in the vision domain. However, the interpretability of the mechanism and properties of the learned representations by such a scheme are so far not well-explored. In this work, through comprehensive experiments and empirical studies on Masked Autoencoders (MAE), we address two critical questions to explore the behaviors of the learned representations: (i) Are the latent representations in Masked Autoencoders linearly separable if the input is a mixture of two images instead of one? This can be concrete evidence used to explain why MAE-learned representations have superior performance on downstream tasks, as proven by many literature impressively. (ii) What is the degree of semantics encoded in the latent feature space by Masked Autoencoders? To explore these two problems, we propose a simple yet effective Interpretable MAE (i-MAE) framework with a two-way image reconstruction and a latent feature reconstruction with distillation loss to help us understand the behaviors inside MAE's structure. Extensive experiments are conducted on CIFAR-10/100, Tiny-ImageNet and ImageNet-1K datasets to verify the observations we discovered. Furthermore, in addition to qualitatively analyzing the characteristics of the latent representations, we examine the existence of linear separability and the degree of semantics in the latent space by proposing two novel metrics. The surprising and consistent results across the qualitative and quantitative experiments demonstrate that i-MAE is a superior framework design for interpretability research of MAE frameworks, as well as achieving better representational ability. Code is available at https://github.com/vision-learning-acceleration-lab/i-mae.

本文介绍了一种名为i-MAE的简单而有效的可解释MAE (Interpretable MAE)框架，通过在CIFAR-10/100，Tiny-ImageNet和ImageNet-1K数据集上进行广泛实验，证明其是解释MAE框架行为的优秀设计，并提供了更好的表示能力。

i-MAE：掩蔽自编码器中的潜在表示是否线性可分？