  In this paper, we propose a noise-aware encoder-decoder framework to
disentangle a clean saliency predictor from noisy training examples, where the
noisy labels are generated by unsupervised handcrafted feature-based methods.
The proposed model consists of two sub-models parameterized by neural networks:
(1) a saliency predictor that maps input images to clean saliency maps, and (2)
a noise generator, which is a latent variable model that produces noises from
Gaussian latent vectors. The whole model that represents noisy labels is a sum
of the two sub-models. The goal of training the model is to estimate the
parameters of both sub-models, and simultaneously infer the corresponding
latent vector of each noisy label. We propose to train the model by using an
alternating back-propagation (ABP) algorithm, which alternates the following
two steps: (1) learning back-propagation for estimating the parameters of two
sub-models by gradient ascent, and (2) inferential back-propagation for
inferring the latent vectors of training noisy examples by Langevin Dynamics.
To prevent the network from converging to trivial solutions, we utilize an
edge-aware smoothness loss to regularize hidden saliency maps to have similar
structures as their corresponding images. Experimental results on several
benchmark datasets indicate the effectiveness of the proposed model.
