论文标题
中断：使用上下文向量的自我监督的视觉属性分离
DisCont: Self-Supervised Visual Attribute Disentanglement using Context Vectors
论文作者
论文摘要
在没有事先监督的情况下删除图像中的基本功能属性是一项具有挑战性的任务。可以很好地归因于属性的模型提供了更大的解释性和控制性。在本文中，我们提出了一个自我监督的框架中断，以通过利用图像中的结构归纳偏见来解散多个属性。由于最近的对比学习范式激增，我们的模型弥合了自我监视的对比学习算法与无监督的杂物之间的差距。我们在定性和定量的四个基准数据集上评估方法的疗效。
Disentangling the underlying feature attributes within an image with no prior supervision is a challenging task. Models that can disentangle attributes well provide greater interpretability and control. In this paper, we propose a self-supervised framework DisCont to disentangle multiple attributes by exploiting the structural inductive biases within images. Motivated by the recent surge in contrastive learning paradigms, our model bridges the gap between self-supervised contrastive learning algorithms and unsupervised disentanglement. We evaluate the efficacy of our approach, both qualitatively and quantitatively, on four benchmark datasets.
