devnag/pytorch-generative-adversarial-networks
A very simple generative adversarial network (GAN) in PyTorch
This project helps machine learning practitioners explore the fundamentals of Generative Adversarial Networks (GANs). It takes a simple dataset, like a Gaussian distribution, and trains two neural networks—one to generate data and one to discriminate between real and generated data. The output is a generated dataset that closely mimics the properties of the original real data. It's ideal for those learning or teaching the core concepts of GANs.
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Use this if you are a machine learning student, researcher, or educator looking for a straightforward, minimal implementation to understand how GANs learn to generate data.
Not ideal if you need a robust, high-performance GAN for complex image generation, deepfakes, or other production-level applications.
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Python
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Apache-2.0
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Jun 30, 2021
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