As the deep learning revolution marches on, self-supervised learning has
garnered increasing attention in recent years thanks to its remarkable
representation learning ability and the low dependence on labeled data. Among
these varied self-supervised techniques, masked modeling has emerged as a
distinctive approach that involves predicting parts of the original data that
are proportionally masked during training. This paradigm enables deep models to
learn robust representations and has demonstrated exceptional performance in
the context of computer vision, natural language processing, and other
modalities. In this survey, we present a comprehensive review of the masked
modeling framework and its methodology. We elaborate on the details of
techniques within masked modeling, including diverse masking strategies,
recovering targets, network architectures, and more. Then, we systematically
investigate its wide-ranging applications across domains. Furthermore, we also
explore the commonalities and differences between masked modeling methods in
different fields. Toward the end of this paper, we conclude by discussing the
limitations of current techniques and point out several potential avenues for
advancing masked modeling research. A paper list project with this survey is
available at url{}.

Expert Commentary: Unleashing the Power of Self-Supervised Learning with Masked Modeling

The field of deep learning has revolutionized many industries by enabling computers to learn from vast amounts of labeled data. However, labeled data can be expensive and time-consuming to obtain. This is where self-supervised learning comes in. Self-supervised learning leverages the inherent structure in data to create labels automatically, reducing the need for manually labeled examples.

Among the various self-supervised learning techniques, masked modeling has gained significant attention. By masking parts of the original data during training and forcing the model to predict the missing parts, masked modeling enables deep models to learn robust representations. This approach has proven particularly effective in domains such as computer vision and natural language processing.

This survey offers a comprehensive review of the masked modeling framework and its methodology. It delves into the different techniques within masked modeling, including various masking strategies, recovering targets, and network architectures. By providing in-depth insights into these techniques, this survey equips researchers and practitioners with a solid understanding of the current state-of-the-art in masked modeling.

One of the strengths of this survey is its multi-disciplinary nature. The concepts and techniques covered in masked modeling are applicable to diverse domains such as computer vision, natural language processing, and others. By exploring the wide-ranging applications of masked modeling, this survey highlights the versatility and potential impact of this approach.

Furthermore, this survey goes beyond just presenting techniques and applications. It also takes a comparative approach by examining the commonalities and differences between masked modeling methods in different fields. This analysis provides valuable insights into how different domains can learn from each other and inspire new advancements in masked modeling research.

However, it is important to acknowledge the limitations of current techniques. While masked modeling has shown exceptional performance, there are still challenges to overcome. The survey rightly points out these limitations and proposes several potential avenues for further research. By identifying research gaps and suggesting future directions, this survey sets the stage for continued advancements in masked modeling.

In conclusion, this survey on masked modeling is an invaluable resource for anyone interested in self-supervised learning and representation learning. Its comprehensive coverage, multi-disciplinary nature, and insightful analysis make it a must-read for researchers, practitioners, and educators alike. With the field of deep learning constantly evolving, masked modeling holds tremendous potential to further advance the capabilities of machine learning systems across a wide range of applications.

For a more detailed exploration of the topics covered in this survey and access to a curated list of relevant papers, I recommend visiting the project’s GitHub repository at

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