  One-class novelty detection is the process of determining if a query example
differs from the training examples (the target class). Most of previous
strategies attempt to learn the real characteristics of target sample by using
generative adversarial networks (GANs) methods. However, the training process
of GANs remains challenging, suffering from instability issues such as mode
collapse and vanishing gradients. In this paper, by adopting non-adversarial
generative networks, a novel decoder-encoder framework is proposed for novelty
detection task, insteading of classical encoder-decoder style. Under the
non-adversarial framework, both latent space and image reconstruction space are
jointly optimized, leading to a more stable training process with super fast
convergence and lower training losses. During inference, inspired by cycleGAN,
we design a new testing scheme to conduct image reconstruction, which is the
reverse way of training sequence. Experiments show that our model has the clear
superiority over cutting-edge novelty detectors and achieves the
state-of-the-art results on the datasets.
