State-of-the-art methods for self-supervised learning (SSL) build representations by maximizing the similarity between different augmented "views" of a sample. Because these approaches try to match views of the same sample, they can be too myopic and fail to produce meaningful results when augmentations are not sufficiently rich. This motivates the use of the dataset itself to find similar, yet distinct, samples to serve as views for one another. In this paper, we introduce Mine Your Own vieW (MYOW), a new approach for building across-sample prediction into SSL. The idea behind our approach is to actively mine views, finding samples that are close in the representation space of the network, and then predict, from one sample's latent representation, the representation of a nearby sample. In addition to showing the promise of MYOW on standard datasets used in computer vision, we highlight the power of this idea in a novel application in neuroscience where rich augmentations are not already established. When applied to neural datasets, MYOW outperforms other self-supervised approaches in all examples (in some cases by more than 10%), and surpasses the supervised baseline for most datasets. By learning to predict the latent representation of similar samples, we show that it is possible to learn good representations in new domains where augmentations are still limited.

介绍自我监督学习的全新方法'Mine Your Own vieW（MYOW）'，用于数据集挖掘，以便为神经科学等新领域建立多元视图。该方法在计算机视觉基准测试中表现出了良好的前景，并在多单元神经记录数据上的测试中表现出更好的性能。

挖掘自己的视角: 自我监督学习通过跨样本预测