论文标题
插槽对比网络：表示对象的对比方法
Slot Contrastive Networks: A Contrastive Approach for Representing Objects
论文作者
论文摘要
从低级视觉数据中提取对象是进一步进步的重要目标。代表无标签对象的现有方法使用带有静态图像的结构化生成模型。这些方法将大量能力集中在重建不重要的背景像素，缺少低对比度或小物体的情况下。相反，我们提出了一种新方法，避免像素空间中的损失以及对静态图像提供的有限信号的过度依赖。我们的方法通过学习插槽表示空间中的判别性，时间对抗性损失来利用对象的运动，试图强迫每个插槽不仅捕获移动的实体，而且还从其他插槽中捕获了不同的对象。此外，我们引入了一个新的定量评估指标，以衡量一组插槽向量的“多样化”，并使用它来评估我们的20个Atari游戏模型。
Unsupervised extraction of objects from low-level visual data is an important goal for further progress in machine learning. Existing approaches for representing objects without labels use structured generative models with static images. These methods focus a large amount of their capacity on reconstructing unimportant background pixels, missing low contrast or small objects. Conversely, we present a new method that avoids losses in pixel space and over-reliance on the limited signal a static image provides. Our approach takes advantage of objects' motion by learning a discriminative, time-contrastive loss in the space of slot representations, attempting to force each slot to not only capture entities that move, but capture distinct objects from the other slots. Moreover, we introduce a new quantitative evaluation metric to measure how "diverse" a set of slot vectors are, and use it to evaluate our model on 20 Atari games.
