Transcription of Training data-efficient image transformers & distillation ...
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Training data-efficient image transformers & distillation ...
Training data-efficient image transformers & distillation through attentionHugo Touvron?, Matthieu Cord Matthijs Douze?Francisco Massa?Alexandre Sablayrolles?Herv e J egou??Facebook AI Sorbonne UniversityAbstractRecently, neural networks purely based on attention were shown to ad-dress image understanding tasks such as image classification. These high-performing vision transformers are pre-trained with hundreds of millionsof images using a large infrastructure, thereby limiting their this work, we produce competitive convolution-free transformers bytraining on Imagenet only. We train them on a single computer in less than3 days. Our reference vision transformer (86M parameters) achieves top-1accuracy of (single-crop) on ImageNet with no external importantly, we introduce a teacher-student strategy specific totransformers.
transformer through attention. This transformer-specific strategy outper-forms vanilla distillation by a significant margin. •Interestingly, with our distillation, image transformers learn more from a convnet than from another transformer with comparable performance. •Our models pre-learned on Imagenet are competitive when transferred to
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