Modality-Agnostic Self-Supervised Learning with Meta-Learned Masked
  Auto-Encoder
Huiwon Jang Jihoon Tack Daewon Choi Jongheon Jeong Jinwoo Shin
Despite its practical importance across a wide range of modalities, recent
advances in self-supervised learning (SSL) have been primarily focused on a few
well-curated domains, e.g., vision and language, often relying on their
domain-specific knowledge. For example, Masked Auto-Encoder (MAE) has become
one of the popular architectures in these domains, but less has explored its
potential in other modalities. In this paper, we develop MAE as a unified,
modality-agnostic SSL framework. In turn, we argue meta-learning as a key to
interpreting MAE as a modality-agnostic learner, and propose enhancements to
MAE from the motivation to jointly improve its SSL across diverse modalities,
coined MetaMAE as a result. Our key idea is to view the mask reconstruction of
MAE as a meta-learning task: masked tokens are predicted by adapting the
Transformer meta-learner through the amortization of unmasked tokens. Based on
this novel interpretation, we propose to integrate two advanced meta-learning
techniques. First, we adapt the amortized latent of the Transformer encoder
using gradient-based meta-learning to enhance the reconstruction. Then, we
maximize the alignment between amortized and adapted latents through task
contrastive learning which guides the Transformer encoder to better encode the
task-specific knowledge. Our experiment demonstrates the superiority of MetaMAE
in the modality-agnostic SSL benchmark (called DABS), significantly
outperforming prior baselines. Code is available at
https://github.com/alinlab/MetaMAE.