Unsupervised_Anomaly_Detection_Using_Convolutional_Autoencoder_Pytorch

Built an unsupervised dataset from a supervised labeled dataset of MNIST dataset by removing its labels. Then defined a Convolutional Autoencoder network in PyTorch and trained it on the unsupervised dataset and allowed the network to learn to reconstruct the training images containing regular image with a small percentage of anomaly images. Then during inference, I set a reconstruction error (MSE) threshold based on a given percent quantile and declared an input image as an anomaly for who's the image reconstruction error is above the preset threshold.