Reviews: Emergence of Object Segmentation in Perturbed Generative Models

This paper presents a generative model of layered object representation where a generator synthesizes a foreground object, a foreground mask and a background to compose an image at the same time while a discriminator tells the composite's realism. To prevent the generator from cheating, i.e. generating the same foreground and background with a random mask, this paper proposes to add random shift to the generated foreground object and its mask. The rationale behind this is that the generated foreground object and its mask must be valid to allow such random shifts while maintaining the realism of the scene. In other words, the foreground can move independently on the background in a valid layered scene. As a result, this paper discovered that a high-quality foreground mask emerges from this layered generative model so that an encoder is trained to predict the mask from an image for object segmentation.