Self-Distillation with No Labels (DINO): How Momentum Teachers, Centering, and Sharpening Emerge Semantic Attention in Vision Transformers
Self-Distillation with No Labels (DINO): How Momentum Teachers, Centering, and Sharpening Emerge Semantic Attention in Vision Transformers When the Vision Transformer (ViT) was introduced by Dosovitskiy et al. in 2020, standard wisdom suggested that transformers required massive supervised corpora (such as JFT-300M) to overcome their lack of convolutional inductive biases. Unlike Convolutional Neural Networks (CNNs), which bake translation equivariance and local receptive fields directly into t
1 min
