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Unsupervised extraction of objects from low-level visual data is an importantgoal for further progress in machine learning. Existing approaches forrepresenting objects without labels use structured generative models withstatic images. These methods focus a large amount of their capacity onreconstructing unimportant background pixels, missing low contrast or smallobjects. Conversely, we present a new method that avoids losses in pixel spaceand over-reliance on the limited signal a static image provides. Our approachtakes advantage of objects' motion by learning a discriminative,time-contrastive loss in the space of slot representations, attempting to forceeach slot to not only capture entities that move, but capture distinct objectsfrom the other slots. Moreover, we introduce a new quantitative evaluationmetric to measure how "diverse" a set of slot vectors are, and use it toevaluate our model on 20 Atari games.

Slot Contrastive Networks: A Contrastive Approach for Representing Objects