Generative Adversarial Networks (GAN) Practice Exams

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Description: Generative Adversarial Networks (GAN) are a class of machine learning frameworks designed to generate new data samples that resemble a given dataset. They consist of two neural networks—the generator and the discriminator—that compete against each other, hence the term "adversarial." The generator creates synthetic data, while the discriminator evaluates whether the data is real or fake. This dynamic creates a continuous improvement loop, allowing the generator to produce increasingly realistic outputs over time.GAN architecture typically includes the generator network, which transforms random noise into data samples, and the discriminator network, which classifies inputs as real or generated. The generator learns to create convincing data, while the discriminator becomes better at spotting fakes. This interplay between the two networks is formulated as a minimax game, where the generator aims to minimize the probability of the discriminator correctly identifying generated samples, and the discriminator aims to maximize it.The training process of GANs can be complex and unstable. It involves iterative updates of both networks using gradient-based optimization techniques. Convergence is not guaranteed, and issues such as mode collapse—where the generator produces limited variety—can occur. Careful tuning of hyperparameters, network architectures, and loss functions is often required to achieve effective training results.Applications of GANs span multiple domains, including image synthesis, video generation, and data augmentation. They are widely used in creating realistic images for creative industries, enhancing low-resolution images, generating 3D models, and even simulating medical images for research purposes. GANs have also found applications in unsupervised learning and anomaly detection, leveraging their ability to model complex data distributions.Challenges in GAN include instability during training, difficulty in evaluating generated outputs, and sensitivity to network archi
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