NeurIPS 2019
Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center
Paper ID:3952
Title:Emergence of Object Segmentation in Perturbed Generative Models

The paper presents a scheme for learning object segmentation from a set of image data without annotation. The main assumption upon which the approach is built is that the location of object segments can be perturbed relative to background. The method is shown to empirically improve segmentation performance on real images of several object categories. All reviewers have found the contributions of this work significant and interesting, both in terms of the methodology (simple but original) and empirical results. Please consider the improvements suggested by reviewers in the final version.