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Generation of MNIST like digits using Conditional Generative Adversarial Nets
Conditional Generative Adversarial Networks (CGANs) extend the capabilities of traditional GANs by conditioning both the generator and discriminator models on additional information, typically class labels or other forms of auxiliary information.
In this project I explore four diffrent approaches to generate MNIST image with general adversial network.
Implementation of HW for IMVFX course in 2021 Fall semester at NYCU.