CNNs (Convolutional Neural Networks) and GANs (Generative Adversarial Networks) are neural network architectures, but they serve different purposes. CNNs are primarily used for feature extraction and classification tasks, while GANs are designed for generating new data that resembles a training dataset. CNNs use convolutional layers to identify patterns in images, making them suitable for tasks like image recognition and segmentation. For example, a CNN might classify a handwritten digit in the MNIST dataset. GANs, on the other hand, consist of two networks: a generator and a discriminator. The generator creates synthetic data, and the discriminator evaluates its authenticity. GANs are often used for tasks like image generation, super-resolution, and style transfer. Unlike CNNs, GANs focus on creating rather than analyzing data.
What is the difference between CNNs and GANs?
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