  One-class novelty detection is conducted to identify anomalous instances,
with different distributions from the expected normal instances. In this paper,
the Generative Adversarial Network based on the Encoder-Decoder-Encoder scheme
(EDE-GAN) achieves state-of-the-art performance. The two factors bellow serve
the above purpose: 1) The EDE-GAN calculates the distance between two latent
vectors as the anomaly score, which is unlike the previous methods by utilizing
the reconstruction error between images. 2) The model obtains best results when
the batch size is set to 1. To illustrate their superiority, we design a new
GAN architecture, and compare performances according to different batch sizes.
Moreover, with experimentation leads to discovery, our result implies there is
also evidence of just how beneficial constraint on the latent space are when
engaging in model training. In an attempt to learn compact and fast models, we
present a new technology, Progressive Knowledge Distillation with GANs
(P-KDGAN), which connects two standard GANs through the designed distillation
loss. Two-step progressive learning continuously augments the performance of
student GANs with improved results over single-step approach. Our experimental
results on CIFAR-10, MNIST, and FMNIST datasets illustrate that P-KDGAN
improves the performance of the student GAN by 2.44%, 1.77%, and 1.73% when
compressing the computationat ratios of 24.45:1, 311.11:1, and 700:1,
respectively.
