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

ViewMix: Augmentation for Robust Representation in Self-Supervised  Learning

The recent emergence of Self-Supervised Learning (SSL) as a fundamental
paradigm for learning image representations has, and continues to, demonstrate
high empirical success in a variety of tasks. However, most SSL approaches fail
to learn embeddings that capture hierarchical semantic concepts that are
separable and interpretable. In this work, we aim to learn highly separable
semantic hierarchical representations by stacking Joint Embedding Architectures
(JEA) where higher-level JEAs are input with representations of lower-level
JEA. This results in a representation space that exhibits distinct
sub-categories of semantic concepts (e.g., model and colour of vehicles) in
higher-level JEAs. We empirically show that representations from stacked JEA
perform on a similar level as traditional JEA with comparative parameter counts
and visualise the representation spaces to validate the semantic hierarchies.

本文介绍了使用堆叠式联合嵌入架构（JEA）来学习高度可分离的语义层次表示的方法，从而产生表现出更具独特的语义概念的表示空间，证明了其在语义分类上的有效性。