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Learning meaningful representations is at the heart of many tasks in the field of modern machine learning.
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An image is worth 16x16 words: Transformers for image recognition at scale
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Knowledge distillation: A survey
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Efficient self-supervised vision transformers for representation learning
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Scaling Local Self-Attention for Parameter Efficient Visual Backbones
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Masked autoencoders are scalable vision learners
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Image data augmentation for deep learning: A survey
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