You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

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.

Question: Why should we care about the batch size in cost of performance on the unsupervised representation learning methods?
Curernt trend of self-supervised learning methods employ a large-scale dataset. We care about batch size since this corresponds to speed of the method.