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To learn visual representations, self-attention-based models, particularly vision transformers, offer an alternative to convolutional neural networks (CNNs). ViT breaks an image into a series of non-overlapping patches and then uses multi-headed self-attention in transformers to learn interpatch representations. The typical trend in ViT networks is to increase the number of parameters to improve p