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Found 7 papers, 5 papers with code
Generative Minimization Networks: Training GANs Without Competition
Among known problems experienced by practitioners is the lack of convergence guarantees or convergence to a non-optimum cycle.
Evaluating GANs via Duality
Generative Adversarial Networks (GANs) have shown great results in accurately modeling complex distributions, but their training is known to be difficult due to instabilities caused by a challenging minimax optimization problem.
A domain agnostic measure for monitoring and evaluating GANs
Evaluations are essential for: (i) relative assessment of different models and (ii) monitoring the progress of a single model throughout training.
Defending Against Adversarial Attacks by Leveraging an Entire GAN
Based on this, we propose a cleaning method which uses both the discriminator and generator of the GAN to project the samples back onto the data manifold.
An Online Learning Approach to Generative Adversarial Networks
We consider the problem of training generative models with a Generative Adversarial Network (GAN).
Neural Multi-Step Reasoning for Question Answering on Semi-Structured Tables
Second, paraphrases of logical forms and questions are embedded in a jointly learned vector space using word and character convolutional neural networks.
Neural Document Embeddings for Intensive Care Patient Mortality Prediction
We present an automatic mortality prediction scheme based on the unstructured textual content of clinical notes.
