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This paper studies the potential of distilling knowledge from pre-trained models, especially Masked Autoencoders.
Imagenet: A large-scale hierarchical image database
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Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2021
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Going deeper with image transformers
Hugo Touvron, Matthieu Cord, Alexandre Sablayrolles, Gabriel Synnaeve, and Hervé Jégou · 2021
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Knowledge distillation via softmax regression representation learning
Jing Yang, Brais Martinez, Adrian Bulat, Georgios Tzimiropoulos, et al · 2021
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Dearkd: Data-efficient early knowledge distillation for vision transformers
Xianing Chen, Qiong Cao, Yujie Zhong, Jing Zhang, Shenghua Gao, and Dacheng Tao · 2022
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Masked autoencoders are scalable vision learners
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ibot: Image bert pre-training with online tokenizer
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