![]() The discriminator takes as input the complete image, either real or generated, and tries to distinguish between them. The generator takes as input the incomplete image and the optional conditioning information, and outputs a complete image. A GAN consists of two components: a generator and a discriminator. Inpainting works by using a neural network, usually a generative adversarial network (GAN), to learn the distribution of the image data and to generate realistic and coherent pixels for the missing or damaged regions. For example, inpainting can be used to fill in the masked areas of an image, to complete the sketch of a face, or to generate an image based on a text description. Inpainting can also be conditioned on different types of information, such as masks, sketches, or text prompts. Inpainting can be applied to different types of images, such as natural scenes, faces, artworks, or text. Inpainting can be seen as a form of image completion, where the input image is incomplete and the output image is complete. ![]() Inpainting is a form of image synthesis, where the goal is to generate realistic and coherent pixels for the missing or damaged regions of an image, while preserving the original context and style. In this guide, you will learn what inpainting is, how it works, and how you can use it for your own projects. Inpainting can be used for various purposes, such as restoring old photos, removing unwanted objects, or creating new content. ![]() ![]() Inpainting is a machine learning task that involves filling in the missing or damaged parts of an image, such as holes, scratches, or occlusions. ![]()
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