  We propose a new strategy to improve the accuracy and robustness of image
classification. First, we train a baseline CNN model. Then, we identify
challenging regions in the feature space by identifying all misclassified
samples, and correctly classified samples with low confidence values. These
samples are then used to train a Variational AutoEncoder (VAE). Next, the VAE
is used to generate synthetic images. Finally, the generated synthetic images
are used in conjunction with the original labeled images to train a new model
in a semi-supervised fashion. Empirical results on benchmark datasets such as
STL10 and CIFAR-100 show that the synthetically generated samples can further
diversify the training data, leading to improvement in image classification in
comparison with the fully supervised baseline approaches using only the
available data.
