  Many real world vision tasks, such as reflection removal from a transparent
surface and intrinsic image decomposition, can be modeled as single image layer
separation. However, this problem is highly ill-posed, requiring accurately
aligned and hard to collect triplet data to train the CNN models. To address
this problem, this paper proposes an unsupervised method that requires no
ground truth data triplet in training. At the core of the method are two
assumptions about data distributions in the latent spaces of different layers,
based on which a novel unsupervised layer separation pipeline can be derived.
Then the method can be constructed based on the GANs framework with
self-supervision and cycle consistency constraints, etc. Experimental results
demonstrate its successfulness in outperforming existing unsupervised methods
in both synthetic and real world tasks. The method also shows its ability to
solve a more challenging multi-layer separation task.
