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Found 3 papers, 1 papers with code
Generative adversarial interpolative autoencoding: adversarial training on latent space interpolations encourages convex latent distributions
We present a neural network architecture based upon the Autoencoder (AE) and Generative Adversarial Network (GAN) that promotes a convex latent distribution by training adversarially on latent space interpolations.
Generative adversarial interpolative autoencoding: adversarial training on latent space interpolations encourage convex latent distributions
We present a neural network architecture based upon the Autoencoder (AE) and Generative Adversarial Network (GAN) that promotes a convex latent distribution by training adversarially on latent space interpolations.
Biologically Inspired Radio Signal Feature Extraction with Sparse Denoising Autoencoders
Automatic modulation classification (AMC) is an important task for modern communication systems; however, it is a challenging problem when signal features and precise models for generating each modulation may be unknown.
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