Generating Images

The idea is to generate the same image through the model for a given sample image. The application is to use the model architecture and complete the occluded(half-filled) image. A basic encoder-decoder and deep CNN encoder-decoder models are implemented from scratch, trained, and analysed on three datasets. The Analysis is also on finding a good size hidden representation of the image for every dataset, which can be used for applications. Some well-known approaches for image generation are Autoencoders, Generative Adversarial Networks(GANs), Auto-Regressive models(PixelRNN, PixelCNN), DRAW.