A convolutional neural-network model of human cochlear mechanics and filter tuning for real-time applications
Implementation of NICE: Nonlinear Independent Components Estimation in Keras
Variational Autoencoders (VAEs)[Kingma, et.al (2013)] let us design complex generative models of data that can be trained on large datasets. This post is about understanding the VAE concepts, its loss functions and how we can implement it in keras.
Often we deal with networks that are optimized for multiple losses (e.g., VAE). In such scenarios, it is useful to keep track of each loss independently, for fine-tuning its contribution to the overall loss.
Tricks to improve SEGAN performance. Eveything is re-implemented into Keras with Tensorflow backend.
A fully convolutional end-to-end speech enhancement system with GANs
Kaldi script for CNN-DNN evaluation on CHiME-3 data