  Unsupervised foreground-background segmentation aims at extracting salient
objects from cluttered backgrounds, where Generative Adversarial Network (GAN)
approaches, especially layered GANs, show great promise. However, without human
annotations, they are typically prone to produce foreground and background
layers with non-negligible semantic and visual confusion, dubbed "information
leakage", resulting in notable degeneration of the generated segmentation mask.
To alleviate this issue, we propose a simple-yet-effective explicit layer
independence modeling approach, termed Independent Layer Synthesis GAN
(ILSGAN), pursuing independent foreground-background layer generation by
encouraging their discrepancy. Specifically, it targets minimizing the mutual
information between visible and invisible regions of the foreground and
background to spur interlayer independence. Through in-depth theoretical and
experimental analyses, we justify that explicit layer independence modeling is
critical to suppressing information leakage and contributes to impressive
segmentation performance gains. Also, our ILSGAN achieves strong
state-of-the-art generation quality and segmentation performance on complex
real-world data. Code is available at: https://github.com/qrzou/ILSGAN
