Unpaired image-to-image translation using cycle-consistent adversarial networks. Ganomaly: Semi-supervised anomaly detection via adversarial training. The GANomaly2D can somehow to capture the abnormal region of the bird and give the high score. The sections below describes the demo in details, including the system model for anomaly detection with RNN, the workflow of developing the PdM demo with step by step instructions, the. As you can see, through the response of anomaly score map at the bottom region is high, some high response at bird region can be found. Processor SDK Linux now provides a predictive maintenance demo which leverages Recurrent Neural Network (RNN) for anomaly detection over motor drive control. The inputs are the image in abnormal domain. The above image illustrates the demo result. Only the score of some patch are high since the region might hard to keep the latent feature consistent. After the iterations of training, the most area of anomaly score map is reduce to 0. The left figure is the input normal image, the middle figure is the reconstruct image by G_E and G_D, and the right figure is the anomaly score map. The above image shows the training result. Python3 demo.py -demo dataset/abnormal/ -batch_size 1 -r 2
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