一种用于极少量标注图像标注与分类的自监督StyleGAN
A Self Supervised StyleGAN for Image Annotation and Classification with Extremely Limited Labels
AI总结：
提出SS-StyleGAN，一种自监督方法，利用StyleGAN潜空间编码器在极少量标注数据（如50或10个样本）下实现强分类性能，并在COVID-19和肝肿瘤病理识别任务中验证优越性。
摘要：
近期基于学习算法的成功在很大程度上归因于用于训练的海量标注数据。然而，由于标注成本高昂，许多数据集缺乏标注，导致深度学习方法的性能下降。自监督学习常被用来减轻对大规模标注数据集的依赖，因为它利用未标注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，一种适用于极少量标注数据集的图像标注与分类的自监督方法。这一新颖框架通过集成一个编码器来学习StyleGAN潜空间的嵌入，从而为StyleGAN架构添加自监督，该潜空间以其解耦特性而闻名。学习到的潜空间能够智能地从数据中选择代表性样本进行标注，以提高分类性能。我们展示了所提方法在使用仅50甚至10个样本的小型标注数据集时取得了强大的分类结果。我们证明了该方法在COVID-19和肝肿瘤病理识别任务上的优越性。
英文摘要：
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.
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