What are the advantages and challenges of using conditional variational autoencoders for image inpainting?
Image inpainting is the task of filling in missing or corrupted regions of an image, such as removing unwanted objects, restoring damaged photos, or completing partial sketches. It is a challenging problem that requires generating realistic and coherent content that matches the style and context of the original image. In this article, you will learn how conditional variational autoencoders (CVAEs) can be used for image inpainting, what are their advantages and challenges, and how they compare to other conditional generative models.
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Khushee KapoorUWaterloo | Master of Data Science and Artificial Intelligence (Co-op) | LinkedIn Top Voice for Data Science | Amongst…
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Minh Chien VuPh.D | Machine learning engineer
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Suman G N| Data analyst | Data scientist | Artificial Intelligence| Certified in Data Science by State University of New York at…