Setting up the Neural Network
Explore the setup of the U-Net neural network architecture for image generation. Understand its contracting and expansive paths, convolutional layers, and how temporal embeddings integrate to predict noise, preparing you for subsequent model training and image sampling.
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Overview
The U-Net architecture is a convolutional neural network (CNN) used for semantic segmentation tasks in image processing. It was developed for image segmentation but has since found applications in various other domains due to its effectiveness in handling datasets and its ability to generate precise segmentation masks.
The architecture is named U-Net due to its U-shaped design. It consists of two main parts: the contracting path (left side) and the expansive path (right side), which are connected by a central bottleneck. The pictorial view of the U-Net structure is as follows:
The contracting path is used for feature extraction and context capture through convolutional and ...