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We tackle the problem of object discovery, where objects are segmented for agiven input image, and the system is trained without using any directsupervision whatsoever. A novel copy-pasting GAN framework is proposed, wherethe generator learns to discover an object in one image by compositing it intoanother image such that the discriminator cannot tell that the resulting imageis fake. After carefully addressing subtle issues, such as preventing thegenerator from `cheating', this game results in the generator learning toselect objects, as copy-pasting objects is most likely to fool thediscriminator. The system is shown to work well on four very differentdatasets, including large object appearance variations in challenging clutteredbackgrounds.

Object Discovery with a Copy-Pasting GAN