  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.
