The recent success of learning-based algorithms can be greatly attributed to the immense amount of annotated data used for training. Yet, many datasets lack annotations due to the high costs associated with labeling, resulting in degraded performances of deep learning methods. Self-supervised learning is frequently adopted to mitigate the reliance on massive labeled datasets since it exploits unlabeled data to learn relevant feature representations. In this work, we propose SS-StyleGAN, a self-supervised approach for image annotation and classification suitable for extremely small annotated datasets. This novel framework adds self-supervision to the StyleGAN architecture by integrating an encoder that learns the embedding to the StyleGAN latent space, which is well-known for its disentangled properties. The learned latent space enables the smart selection of representatives from the data to be labeled for improved classification performance. We show that the proposed method attains strong classification results using small labeled datasets of sizes 50 and even 10. We demonstrate the superiority of our approach for the tasks of COVID-19 and liver tumor pathology identification.

中文翻译：

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用于标签极其有限的图像注释和分类的自监督 StyleGAN


基于学习的算法最近的成功很大程度上归功于用于训练的大量注释数据。然而，由于标记成本高昂，许多数据集缺乏注释，导致深度学习方法的性能下降。自监督学习经常被用来减轻对大量标记数据集的依赖，因为它利用未标记的数据来学习相关的特征表示。在这项工作中，我们提出了 SS-StyleGAN，这是一种适用于极小注释数据集的图像注释和分类的自监督方法。这种新颖的框架通过集成学习嵌入到 StyleGAN 潜在空间的编码器，为 StyleGAN 架构添加了自我监督，该编码器以其解缠结的特性而闻名。学习到的潜在空间能够从要标记的数据中智能选择代表，以提高分类性能。我们表明，所提出的方法使用大小为 50 甚至 10 的小型标记数据集获得了强大的分类结果。我们证明了我们的方法在 COVID-19 和肝脏肿瘤病理学识别任务中的优越性。