Joint Embedding Architecture-based self-supervised learning methods have attributed the composition of data augmentations as a crucial factor for their strong representation learning capabilities. While regional dropout strategies have proven to guide models to focus on lesser indicative parts of the objects in supervised methods, it hasn't been adopted by self-supervised methods for generating positive pairs. This is because the regional dropout methods are not suitable for the input sampling process of the self-supervised methodology. Whereas dropping informative pixels from the positive pairs can result in inefficient training, replacing patches of a specific object with a different one can steer the model from maximizing the agreement between different positive pairs. Moreover, joint embedding representation learning methods have not made robustness their primary training outcome. To this end, we propose the ViewMix augmentation policy, specially designed for self-supervised learning, upon generating different views of the same image, patches are cut and pasted from one view to another. By leveraging the different views created by this augmentation strategy, multiple joint embedding-based self-supervised methodologies obtained better localization capability and consistently outperformed their corresponding baseline methods. It is also demonstrated that incorporating ViewMix augmentation policy promotes robustness of the representations in the state-of-the-art methods. Furthermore, our experimentation and analysis of compute times suggest that ViewMix augmentation doesn't introduce any additional overhead compared to other counterparts.

通过引入ViewMix增强策略，该研究提出了一种特别为自监督学习设计的方法，在生成同一图像的不同视图时，将图像的补丁从一个视图剪切并粘贴到另一个视图，通过利用这种增强策略所创建的不同视图，多种基于联合嵌入的自监督学习方法具有更好的定位能力并在一致性上胜过相应的基准方法，实验证明引入ViewMix增强策略有助于提高目前最先进方法中的表示的鲁棒性，并且计算时间的实验和分析表明，与其他对应方法相比，引入ViewMix增强策略没有引入额外的开销。

ViewMix: 强化自监督学习中稳健表征的增广