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Found 4 papers, 1 papers with code
Fortifying Fully Convolutional Generative Adversarial Networks for Image Super-Resolution Using Divergence Measures
Super-Resolution (SR) is a time-hallowed image processing problem that aims to improve the quality of a Low-Resolution (LR) sample up to the standard of its High-Resolution (HR) counterpart.
Concurrent Density Estimation with Wasserstein Autoencoders: Some Statistical Insights
Variational Autoencoders (VAEs) have been a pioneering force in the realm of deep generative models.
Interval Bound Interpolation for Few-shot Learning with Few Tasks
We then use a novel strategy to artificially form new tasks for training by interpolating between the available tasks and their respective interval bounds.
Statistical Regeneration Guarantees of the Wasserstein Autoencoder with Latent Space Consistency
The introduction of Variational Autoencoders (VAE) has been marked as a breakthrough in the history of representation learning models.
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