Self-supervised learning (SSL) using mixed images has been studied to learn various image representations. Existing methods using mixed images learn a representation by maximizing the similarity between the representation of the mixed image and the synthesized representation of the original images. However, few methods consider the synthesis of representations from the perspective of mathematical logic. In this study, we focused on a synthesis method of representations. We proposed a new SSL with mixed images and a new representation format based on many-valued logic. This format can indicate the feature-possession degree, that is, how much of each image feature is possessed by a representation. This representation format and representation synthesis by logic operation realize that the synthesized representation preserves the remarkable characteristics of the original representations. Our method performed competitively with previous representation synthesis methods for image classification tasks. We also examined the relationship between the feature-possession degree and the number of classes of images in the multilabel image classification dataset to verify that the intended learning was achieved. In addition, we discussed image retrieval, which is an application of our proposed representation format using many-valued logic.

使用混合图像的自监督学习研究了各种图像表征。我们提出了一种基于多值逻辑的新型混合图像的自监督学习方法和新的表征格式，该格式可以指示特征占有程度，即每个图像特征被表征所占有的程度。此方法在图像分类任务中与以前的表征合成方法表现竞争力，并且还通过多标签图像分类数据集验证了特征占有程度与图像类别数量之间的关系，以验证其预期的学习成果。另外，我们还讨论了图像检索，这是使用多值逻辑的我们提出的表征格式的一个应用。

通过概率多值逻辑操作在自监督学习中实现表示合成