  We propose an unsupervised foreground-background segmentation method via
training a segmentation network on the synthetic pseudo segmentation dataset
generated from GANs, which are trained from a collection of images without
annotations to explicitly disentangle foreground and background. To efficiently
generate foreground and background layers and overlay them to compose novel
images, the construction of such GANs is fulfilled by our proposed Equivariant
Layered GAN, whose improvement, compared to the precedented layered GAN, is
embodied in the following two aspects. (1) The disentanglement of foreground
and background is improved by extending the previous perturbation strategy and
introducing private code recovery that reconstructs the private code of
foreground from the composite image. (2) The latent space of the layered GANs
is regularized by minimizing our proposed equivariance loss, resulting in
interpretable latent codes and better disentanglement of foreground and
background. Our methods are evaluated on unsupervised object segmentation
datasets including Caltech-UCSD Birds and LSUN Car, achieving state-of-the-art
performance.
