adversarial autoencoders
Adversarial autoencoders (AAEs) are a type of deep learning model that combines generative autoencoders with adversarial training. They consist of two main components: an encoder that maps input data to a lower-dimensional latent space, and a decoder that reconstructs the original data from the latent representation. AAEs are trained using a discriminative network, known as the adversary, which aims to distinguish between true data and reconstructed data. By optimizing the autoencoder to confuse the adversary and reconstruct data that is difficult to differentiate, AAEs can generate more realistic and diverse synthetic data.
Requires login.
Related Concepts (1)
Similar Concepts
- adversarial anomaly detection
- adversarial attacks
- adversarial deep learning
- adversarial feature learning
- adversarial image classification
- adversarial image synthesis
- adversarial input synthesis
- adversarial machine learning
- adversarial reinforcement learning
- adversarial robustness
- adversarial text generation
- adversarial training
- autoencoders
- generative adversarial networks
- variational autoencoders