PixelRNNs are a type of neural network that can create realistic images by predicting the pixels in an image pixel by pixel. They use complex mathematical algorithms and models to generate images that are similar to those found in real life.
How do PixelRNNs Work?
PixelRNNs are trained on vast datasets of images and learn to generate new images by predicting pixel values based on the colors and shapes present in the training data. The network starts at the top-left pixel of an image and predicts the color of that pixel based on information it has learned from the training data. It then moves to the next pixel to the right and uses the information it has gathered from the previous pixels to predict its color, and so on until the entire image is generated.
PixelRNNs get their name from the fact that they model the probability distributions of pixels in an image as a sequence of pixels along the two spatial dimensions, which are typically the x and y coordinates of the image. By predicting each pixel sequentially, PixelRNNs can capture the complex dependencies between pixels that exist in real images.
What are the Variants of PixelRNN?
The two main variants of PixelRNNs are Row LSTM and Diagonal BiLSTM. The Row LSTM variant optimizes for creating images with a small number of large objects by focusing on generating each row of the image independently, whereas the Diagonal BiLSTM variant optimizes for creating images with lots of small details by focusing on generating smaller patches of the image at once.
How are Pixel Values Treated in PixelRNNs?
Pixel values are treated as discrete random variables in PixelRNNs. This means that they are represented as individual probability distributions that predict the likelihood of each possible pixel value for a given pixel. These distributions are modeled using a softmax layer, which calculates the probabilities of each possible pixel value given the information that the network has learned from the training data.
Masked convolutions are used in PixelRNNs to help model the full dependencies between color channels. This allows the network to generate more accurate predictions of pixel values that take into account the color information present in the rest of the image.
PixelRNNs are an exciting development in the field of artificial intelligence and computer vision. By modeling the probability distributions of pixel values and capturing the complex dependencies between pixels in an image, they can generate visually stunning images that are indistinguishable from those created by humans. While still in the early stages of development, PixelRNNs have the potential to transform the way we create and experience visual content.
