Swapping Semantic Contents for Mixing Images

• URL: http://arxiv.org/abs/2205.10158v1
• Date: Fri, 20 May 2022 13:07:27 GMT
• Title: Swapping Semantic Contents for Mixing Images
• Authors: R\'emy Sun, Cl\'ement Masson, Gilles H\'enaff, Nicolas Thome, Matthieu Cord
• Abstract summary: Mixing Data Augmentations do not typically yield new labeled samples, as indiscriminately mixing contents creates between-class samples. We introduce the SciMix framework that can learn to generator to embed a semantic style code into image backgrounds. We demonstrate that SciMix yields novel mixed samples that inherit many characteristics from their non-semantic parents.
• Score: 44.0283695495163
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep architecture have proven capable of solving many tasks provided a sufficient amount of labeled data. In fact, the amount of available labeled data has become the principal bottleneck in low label settings such as Semi-Supervised Learning. Mixing Data Augmentations do not typically yield new labeled samples, as indiscriminately mixing contents creates between-class samples. In this work, we introduce the SciMix framework that can learn to generator to embed a semantic style code into image backgrounds, we obtain new mixing scheme for data augmentation. We then demonstrate that SciMix yields novel mixed samples that inherit many characteristics from their non-semantic parents. Afterwards, we verify those samples can be used to improve the performance semi-supervised frameworks like Mean Teacher or Fixmatch, and even fully supervised learning on a small labeled dataset.

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