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hankabc's Projects

caffe

Caffe for Sparse and Low-rank Deep Neural Networks

csgm

Code to reproduce results from the paper: "Compressed Sensing using Generative Models".

csmri-refinement

Code for "Adversarial and Perceptual Refinement Compressed Sensing MRI Reconstruction"

dagan

The implementation code for "DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction"

dcgan-completion.tensorflow

Image Completion with Deep Learning in TensorFlow

deepvideocs

PyTorch deep learning framework for video compressive sensing.

gan

Resources and Implementations of Generative Adversarial Nets: GAN, DCGAN, WGAN, CGAN, InfoGAN

gancs

Compressed Sensing MRI based on Deep Generative Adversarial Network

lbpcascade_animeface

A Face detector for anime/manga using OpenCV

Simulation code for "Enabling Large Intelligent Surfaces with Compressive Sensing and Deep Learning" by Abdelrahman Taha, Muhammad Alrabeiah, Ahmed Alkhateeb, arXiv e-prints, p. arXiv:1904.10136, Apr 2019.

pytorch-book

PyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation

pytorch-gan

PyTorch implementations of Generative Adversarial Networks.

pytorch-gan-timeseries

GANs for time series generation in pytorch

pytorch-vqvae

Vector Quantized VAEs - PyTorch Implementation

reproducible-deep-compressive-sensing

Collection of reproducible deep learning for compressive sensing

rgan

Recurrent (conditional) generative adversarial networks for generating real-valued time series data.

semisupervised_timeseries_infogan

A tensorflow implementation of informative generative adversarial network (InfoGAN ) to one dimensional ( 1D ) time series data with a supervised loss function. So it's called semisupervised Info GAN.

speech_signal_processing_and_classification

Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8].

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