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Masked Autoencoders is a simple yet powerful self-supervised learning method.However, it learns representations indirectly by reconstructing masked inputpatches. Several methods learn representations directly by predictingrepresentations of masked patches; however, we think using all patches toencode training signal representations is suboptimal. We propose a new method,Masked Modeling Duo (M2D), that learns representations directly while obtainingtraining signals using only masked patches. In the M2D, the online networkencodes visible patches and predicts masked patch representations, and thetarget network, a momentum encoder, encodes masked patches. To better predicttarget representations, the online network should model the input well, whilethe target network should also model it well to agree with online predictions.Then the learned representations should better model the input. We validatedthe M2D by learning general-purpose audio representations, and M2D set newstate-of-the-art performance on tasks such as UrbanSound8K, VoxCeleb1,AudioSet20K, GTZAN, and SpeechCommandsV2. We additionally validate theeffectiveness of M2D for images using ImageNet-1K in the appendix.

Masked Modeling Duo: Learning Representations by Encouraging Both Networks to Model the Input