Online Bag-of-Visual-Words Generation for Unsupervised Representation Learning

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Emerging Properties in Self-Supervised Vision Transformers (2021, background): “Several other works echo this direction, showing that one can match more elaborate representations [26, 27], train features matching them to a uniform distribution [6] or by using whitening [23, 81].”
VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning (2021, neutral): “In OBoW Gidaris et al. (2020) and Gidaris et al. (2021) the dictionary is obtained by off-line or on-line clustering.”
Concept Generalization in Visual Representation Learning (2020, background): “3.4 for the justification of our protocol and the choice of metrics.”
Context Autoencoder for Self-supervised Representation Learning (2022, unknown): “The typical MIM methods, such as BEiT [4], the method studied in the ViT paper [26], and iBoT [104], use a single ViT architecture to solve the pretraining task i.e., reconstructing the patch tokens or the pixel colors.”
AAVAE: Augmentation-Augmented Variational Autoencoders (2021, background): “These algorithms can be classified into two groups: clustering-based [5, 2, 6] and distillation-based [29, 9, 25, 7] approaches.”