Full Implementation and details Project on GitHub Problem Statement Design and train a network that combines supervised and unsupervised architecture in one model to achieve a classification task Dataset Strictly Imbalance CIFAR-10 dataset Architecture Convolutional Neural Network based AutoEncoder Encoder architecture as classifer with additional dense layer to classify images Handling Imbalance dataset Proper distribution of weights for each class Data augmentation emphasize on minority classes AutoEncoder Training Results Reconstruction of images with Mean-Squared-Error loss Classifier Training Scenarios Experiment Results Accuracy on CIFAR-10 test dataset on 10,000 images Training Methodology Xavier Init Pretrained Autoencoder Init Avg Improvement Data Augmentation SGD 0.