标题： 自监督孪生自动编码器 显示英文标题

标题： Self-Supervised Siamese Autoencoders

摘要： 与全监督模型相比，自监督表示学习只需要一小部分数据被标记，并且通常能够实现相同甚至更高的下游性能。 目标是在自监督任务上预训练深度神经网络，使其能够从原始输入数据中提取有意义的特征。 之前，自动编码器和孪生网络已被成功用作图像分类等任务的特征提取器。 然而，两者都有各自的缺点和优点。 在本文中，我们通过提出一种新的方法称为SidAE（孪生去噪自动编码器）来结合它们的互补优势。 使用图像分类下游任务，我们展示了我们的模型在多个数据集和场景中优于两种自监督基线。 至关重要的是，这包括只有少量标记数据可用的情况。 经验表明，孪生组件影响更大，但去噪自动编码器仍然是提高性能所必需的。 显示英文摘要

摘要： In contrast to fully-supervised models, self-supervised representation learning only needs a fraction of data to be labeled and often achieves the same or even higher downstream performance. The goal is to pre-train deep neural networks on a self-supervised task, making them able to extract meaningful features from raw input data afterwards. Previously, autoencoders and Siamese networks have been successfully employed as feature extractors for tasks such as image classification. However, both have their individual shortcomings and benefits. In this paper, we combine their complementary strengths by proposing a new method called SidAE (Siamese denoising autoencoder). Using an image classification downstream task, we show that our model outperforms two self-supervised baselines across multiple data sets and scenarios. Crucially, this includes conditions in which only a small amount of labeled data is available. Empirically, the Siamese component has more impact, but the denoising autoencoder is nevertheless necessary to improve performance.