Beyond CNNs: Encoded Context for Image Inpainting with LSTMs and Pixel CNNs

Our paper presents some creative advancements in the image in-painting techniques for small, simple images for example from the CIFAR10 dataset. This study primarily targeted on improving the performance of the context encoders through the utilization of several major training methods on Generative Adversarial Networks (GANs). To achieve this, we upscaled the network Wasserstein GAN (WGAN) and compared the discriminators and encoders with the current state-of-the-art models, alongside standard Convolutional Neural Network (CNN) architectures. Side by side to this, we also explored methods of Latent Variable Models and developed several different models, namely Pixel CNN, Row Long Short Term Memory (LSTM), and Diagonal Bidirectional Long Short-Term Memory (BiLSTM). Moreover, we proposed a model based on the Pixel CNN architectures and developed a faster yet easy approach called Row-wise Flat Pixel LSTM. Our experiments demonstrate that the proposed models generate high-quality images on CIFAR10 while conformingthe L2 loss and visual quality measurement