Vision
Pixel recurrent neural network (Pixel RNN)
Pixel Recurrent Neural Networks (PixelRNNs) are a type of deep neural network used for image generation. They sequentially predict the pixels in an image, conditioned on the pixels generated before, allowing the model to capture long-range dependencies and complex textures.
Explanation
PixelRNNs model the conditional probability distribution of each pixel given all previous pixels in the image. The network processes the image in a raster scan order (left-to-right, top-to-bottom). At each step, the model predicts the red, green, and blue (RGB) values of the current pixel based on the RGB values of all previously generated pixels. This is achieved using recurrent layers (typically LSTMs or similar recurrent architectures) that capture dependencies between pixels. Variants like PixelCNN use convolutional layers instead of recurrent layers to speed up training and inference. PixelRNNs can generate high-quality images but are computationally expensive due to their sequential nature, which limits their scalability. They are important because they demonstrate the ability of deep learning models to generate complex data like images in a sequential manner, which influenced later generative models.