adversarial networks
Adversarial networks, also known as GANs (Generative Adversarial Networks), are a type of machine learning model consisting of two interconnected networks - a generator and a discriminator. These networks engage in a competitive learning process, where the generator aims to generate synthetic data that replicates the real data, while the discriminator evaluates and distinguishes between the real and generated data. Through this adversarial interaction, GANs can learn to generate high-quality and realistic data.
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Related Concepts (21)
- adversarial anomaly detection
- adversarial attacks
- adversarial autoencoders
- adversarial deep learning
- adversarial detection and defense
- adversarial examples
- adversarial feature learning
- adversarial image classification
- adversarial image synthesis
- adversarial input synthesis
- adversarial machine learning
- adversarial perturbations
- adversarial privacy attacks
- adversarial reinforcement learning
- adversarial risk analysis
- adversarial robustness
- adversarial text generation
- adversarial training
- adversarial transferability
- generative adversarial networks (gans)
- generative models
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