mvisionai
A machine learning Researcher and Engineer; primary focus on data streams and health.
SolveMLInternet
Pinned Repositories
100-Days-Of-ML-Code
100 Days of ML Coding
3D_Octave_Convolution
3D Octave Convolutional Attention Network
ADNI_MODEL
AdSpike
Classification of Alzheimer’s Disease Using Deep Convolutional Spiking Neural Network
cropml_tensorflow_lite
A machine learning android app to predict favorable crops for a cultivable land based on temperature and humidity
FedAD
FedAD: Reducing Non-IDD in Alzheimer's Disease Classification with Adversarial Federated Learning
FedLimited
Semi-supervised Federated Learning on Evolving Data Streams
FedStream
FedStream: Prototype-Based Federated Learning on Distributed Concept-drifting Data Streams
GANs-Public
Course notebooks for GANs specializations
PyTorch-VAE
A Collection of Variational Autoencoders (VAE) in PyTorch.
mvisionai's Repositories
mvisionai/AdSpike
Classification of Alzheimer’s Disease Using Deep Convolutional Spiking Neural Network
mvisionai/FedAD
FedAD: Reducing Non-IDD in Alzheimer's Disease Classification with Adversarial Federated Learning
mvisionai/GANs-Public
Course notebooks for GANs specializations
mvisionai/PyTorch-VAE
A Collection of Variational Autoencoders (VAE) in PyTorch.
mvisionai/ALAE
[CVPR2020] Adversarial Latent Autoencoders
mvisionai/alzheimer-project
mvisionai/AsynDGAN
AsynDGAN project source code.
mvisionai/awesome-selfhosted
A list of Free Software network services and web applications which can be hosted locally. Selfhosting is the process of hosting and managing applications instead of renting from Software-as-a-Service providers
mvisionai/BrainImageNet
mvisionai/CmpE-491-Baris-Basmak
mvisionai/CovidPrognosis
COVID deterioration prediction based on chest X-ray radiographs via MoCo-trained image representations
mvisionai/DCGAN-LSGAN-WGAN-GP-DRAGAN-Tensorflow-2
DCGAN LSGAN WGAN-GP DRAGAN Tensorflow 2
mvisionai/deeplearning.ai-GANs-Specialization
A Generative Adversarial Networks (GANs) Specialization made by deeplearning.ai on Coursera
mvisionai/Django-channels-Tic-Tac-Toe
mvisionai/DRL-Energy-Management
Deep reinforcement learning based energy management strategy for hybrid electric vehicle
mvisionai/faceswap
Deepfakes Software For All
mvisionai/Fed_ABIDE
impelmentation of https://arxiv.org/pdf/2001.05647.pdf
mvisionai/FedDG-ELCFS
[CVPR'21] FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space
mvisionai/Federated-Learning-PyTorch
Implementation of Communication-Efficient Learning of Deep Networks from Decentralized Data
mvisionai/FedMAX
Source code for ECML-PKDD (2020) paper: FedMAX: Mitigating Activation Divergence for Accurate and Communication-Efficient Federated Learning
mvisionai/gan-debiasing
Fair Attribute Classification through Latent Space De-biasing (CVPR 2021)
mvisionai/KiU-Net-pytorch
Official Pytorch Code of KiU-Net for Image Segmentation - MICCAI 2020 (Oral)
mvisionai/MLSAkNN
Self-Adjusting k Nearest Neighbors for Multi-Label Drifting Data Streams
mvisionai/non_iid_dml
mvisionai/PyGrid
A Peer-to-peer Platform for Secure, Privacy-preserving, Decentralized Data Science
mvisionai/PySyft
A library for answering questions using data you cannot see
mvisionai/Regularized_autoencoders-RAE-
mvisionai/streamlit
Streamlit — The fastest way to build data apps in Python
mvisionai/stylegan
StyleGAN - Official TensorFlow Implementation
mvisionai/svoice
We provide a PyTorch implementation of the paper Voice Separation with an Unknown Number of Multiple Speakers In which, we present a new method for separating a mixed audio sequence, in which multiple voices speak simultaneously. The new method employs gated neural networks that are trained to separate the voices at multiple processing steps, while maintaining the speaker in each output channel fixed. A different model is trained for every number of possible speakers, and the model with the largest number of speakers is employed to select the actual number of speakers in a given sample. Our method greatly outperforms the current state of the art, which, as we show, is not competitive for more than two speakers.