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Context: There has been a growing interest in using a large-scale dataset to build powerful machine learning models Radford et al. (2021).Self-supervised learning (SSL), which aims to learn a useful representation without labels, is suitable for this trend; it is actively studied in the fields of natural language processing Devlin et al. (2019); Du et al. (2021) and computer vision Chen et al. (2020a); He et al. (2020).In the vision domain, recent SSL methods commonly use data augmentations and induce their visual representation to be augmentation-invariant.They have achieved state-of-the-art performance surpassing supervised representation in a variety of visual tasks, including semi-supervised learning Caron et al. (2020); Zbontar et al. (2021), transfer learning Ericsson et al. (2021), and object detection Chen et al. (2020c). We apply MIRA to clustering-based representation learning and verify the representation quality on several standard self-supervised learning benchmarks.We demonstrate its state-of-the-art performance on linear/k-NN evaluation, semi-supervised learning, and transfer learning benchmark.We further experiment with convergence speed, scalability, and different components of our method. SSL methods are designed to learn the representation by solving pretext tasks, and recent state-of-the-art methods encourage their learned representations to be augmentation invariant.They are based on various pretext tasks: instance discrimination Chen et al. (2020a, b, c, 2021), metric learning Grill et al. (2020); Chen and He (2021), self-training Zheng et al. (2021); Caron et al. (2021), and clustering Asano et al. (2020); Caron et al. (2018, 2020); only a few account for encoding the semantic structure of data.While some works Wang et al. (2020); Dwibedi et al. (2021); Koohpayegani et al. (2021) consider the nearest neighbors in the latent space, our method belongs to the clustering-based SSL method that flexibly accounts for inter-data similarity.Meanwhile, many SSL methods are prone to collapsing into a trivial solution where every representation is mapped into a constant vector.Various schemes and mechanisms are suggested to address this, e.g., the asymmetric structure, redundancy reduction, etc.We will review more relevant works in detail below.

Question: Is it possible to say the proposed method learns better representation? What is the meaning of the downstream tasks?
SSL methods learn useful representation by solving pretext tasks without labels. In P7, we can get a hint that this benchmarks are testbed for evaluating SSL methods.