Unsupervised approaches for learning representations invariant to common transformations are used quite often for object recognition. Learning invariances makes models more robust and practical to use in real-world scenarios. Since data transformations that do not change the intrinsic properties of the object cause the majority of the complexity in recognition tasks, models that are invariant to these transformations help reduce the amount of training data required. This further increases the model's efficiency and simplifies training. In this paper, we investigate the generalization of invariant representations on out-of-distribution data and try to answer the question: Do model representations invariant to some transformations in a particular seen domain also remain invariant in previously unseen domains? Through extensive experiments, we demonstrate that the invariant model learns unstructured latent representations that are robust to distribution shifts, thus making invariance a desirable property for training in resource-constrained settings.

翻译：无监督学习对常见变换具有不变性的表示方法常被用于目标识别任务。学习不变性使模型在实际场景中更加鲁棒且实用。由于不改变物体固有性质的数据变换是导致识别任务复杂性的主要因素，对这类变换具有不变性的模型有助于减少所需的训练数据量，从而进一步提升模型效率并简化训练过程。本文研究了不变表示在分布外数据上的泛化能力，并试图回答以下问题：在特定可见域中对某些变换具有不变性的模型表示，在未见过的领域是否仍能保持这种不变性？通过大量实验，我们证明不变性模型能够学习到对分布偏移具有鲁棒性的非结构化潜在表示，这使得不变性成为资源受限环境下训练的理想特性。