Fast and Efficient Image Generation Using Variational Autoencoders and K-Nearest Neighbor OveRsampling Approach

Abstract

Researchers gravitate towards Generative Adversarial Networks (GAN) to create artificial images. However, GANs suffer from convergence issues, mode collapse, and overall complexity in balancing the Nash Equilibrium. Images generated are often distorted, rendering them useless. We propose a combination of Variational Autoencoders (VAEs) and a statistical oversampling method called K-Nearest Neighbor OveRsampling (KNNOR) to create artificial images. This combination of VAE and KNNOR results in more life-like images with reduced distortion. We fine-tune several pre-trained networks on a separate set of real and fake face images to test images generated by our method against images generated by conventional Deep Convolutional GANs (DCGANs). We also compare the combination of VAEs and Synthetic Minority Oversampling Technique (SMOTE) to establish the efficacy of KNNOR against naive oversampling methods. Not only are our methods better able to convince the classifiers that the images generated are authentic, but the models are also half in size of DCGANs. The code is available at GitHub for public use.