quality:corruption
Trainable segmentation

U-Net is a convolutional neural network which takes as input an image and outputs a label for each pixel. U-Net initially was developed to detect cell boundaries in biomedical images. U-Net follows classical autoencoder architecture, as such it contains two sub-structures. The encoder structure follows the traditional stack of convolutional and max pooling layers to increase the receptive field as it goes through the layers. It is used to capture the context in the image. The decoder structure utilizes transposed convolution layers for upsampling so that the end dimensions are close to that of the input image. Skip connections are placed between convolution and transposed convolution layers of the same shape in order to preserve details that would have been lost otherwise.