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A web-app based on Wasserstein Generative Adversarial Network architecture with GP that generates multiple realistic paintings, trained on 8k Albrecht Dürer's paintings, includes super-res mode.
Deep neural networks have garnered tremendous excitement in recent years thanks to their superior learning capacity in the presence of abundant data resources. However, collecting an exhaustive dataset covering all possible scenarios is often slow, expensive, and even impractical. The goal of this project is to devise a new learning framework th…
This project is an exploration of Generative Models (GM) and its capabilities, focusing on the generation of bicycle images using Wasserstein Generative Adversarial Networks (WGAN-GP) in conjunction with estimators and generators.