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Found 5 papers, 4 papers with code
SCP-GAN: Self-Correcting Discriminator Optimization for Training Consistency Preserving Metric GAN on Speech Enhancement Tasks
In recent years, Generative Adversarial Networks (GANs) have produced significantly improved results in speech enhancement (SE) tasks.
Ranked #1 on
Speech Enhancement
on VoiceBank + DEMAND
Breaking Time Invariance: Assorted-Time Normalization for RNNs
Methods such as Layer Normalization (LN) and Batch Normalization (BN) have proven to be effective in improving the training of Recurrent Neural Networks (RNNs).
Orthogonal Gated Recurrent Unit with Neumann-Cayley Transformation
In recent years, using orthogonal matrices has been shown to be a promising approach in improving Recurrent Neural Networks (RNNs) with training, stability, and convergence, particularly, to control gradients.
Symmetry Structured Convolutional Neural Networks
We consider Convolutional Neural Networks (CNNs) with 2D structured features that are symmetric in the spatial dimensions.
Adaptive Weighted Discriminator for Training Generative Adversarial Networks
Generative adversarial network (GAN) has become one of the most important neural network models for classical unsupervised machine learning.
Ranked #4 on
Conditional Image Generation
on CIFAR-100
