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For the task of image classification, neural networks primarily rely onvisual patterns. In robust networks, we would expect for visually similarclasses to be represented similarly. We consider the problem of whensemantically similar classes are visually dissimilar, and when visualsimilarity is present among non-similar classes. We propose a data augmentationtechnique with the goal of better aligning semantically similar classes witharbitrary (non-visual) semantic relationships. We leverage recent work indiffusion-based semantic mixing to generate semantic hybrids of two classes,and these hybrids are added to the training set as augmented data. We evaluatewhether the method increases semantic alignment by evaluating model performanceon adversarially perturbed data, with the idea that it should be easier for anadversary to switch one class to a similarly represented class. Resultsdemonstrate that there is an increase in alignment of semantically similarclasses when using our proposed data augmentation method.

Addressing Discrepancies in Semantic and Visual Alignment in Neural Networks