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Poster
Adversarial Masking for Self-Supervised Learning
Yuge Shi · Siddharth N · Phil Torr · Adam Kosiorek

Thu Jul 21 03:00 PM -- 05:00 PM (PDT) @ Hall E #331
in Poster Session 3 »

We propose ADIOS, a masked image model (MIM) framework for self-supervised learning, which simultaneously learns a masking function and an image encoder using an adversarial objective. The image encoder is trained to minimise the distance between representations of the original and that of a masked image. The masking function, conversely, aims at maximising this distance. ADIOS consistently improves on state-of-the-art self-supervised learning (SSL) methods on a variety of tasks and datasets---including classification on ImageNet100 and STL10, transfer learning on CIFAR10/100, Flowers102 and iNaturalist, as well as robustness evaluated on the backgrounds challenge (Xiao et al., 2021)---while generating semantically meaningful masks. Unlike modern MIM models such as MAE, BEiT and iBOT, ADIOS does not rely on the image-patch tokenisation construction of Vision Transformers, and can be implemented with convolutional backbones. We further demonstrate that the masks learned by ADIOS are more effective in improving representation learning of SSL methods than masking schemes used in popular MIM models.

Keywords: APP: Computer Vision · DL: Other Representation Learning · DL: Self-Supervised Learning · MISC: Representation Learning · MISC: Unsupervised and Semi-supervised Learning

Author Information

Yuge Shi (University of Oxford)
Siddharth N (University of Edinburgh)
Phil Torr (Oxford)
Adam Kosiorek (DeepMind)

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