Numan Celik's repositories
TimeSeries-GAN
Generation of Time Series data using generatuve adversarial networks (GANs) for biological purposes.
anomaly-detection
Anomaly detection is a common problem that is applied to machine learning/deep learning research. Here we will apply an LSTM autoencoder (AE) to identify ECG anomaly detections. In our experiments, anomaly detection problem is a rare-event classification problem. Therefore we will train our LSTM AE with major class, then we would have a higher mean squared error when model sees a minor class in the dataset.
vision-transformer-pytorch
Pytorch version of Vision Transformer (ViT) with pretrained models. This is part of CASL (https://casl-project.github.io/) and ASYML project.
AdaptSegNet
Learning to Adapt Structured Output Space for Semantic Segmentation, CVPR 2018 (spotlight)
Awesome-Self-Supervised-Papers
Paper bank for Self-Supervised Learning
cell-segmentation
In this work, cell segmentation was carried out through U-net. Segmented microscopic images were introduced to the neural network model with an annotation tool using OpenCV.
Deep-Channel
To follow
GAN_TimeSeries_Classification
TEST1:This is the test repo for time series classification using GAN.
mvts_transformer
Multivariate Time Series Transformer, public version
pretrained-models.pytorch
Pretrained ConvNets for pytorch: NASNet, ResNeXt, ResNet, InceptionV4, InceptionResnetV2, Xception, DPN, etc.
pytorch-fcn
PyTorch Implementation of Fully Convolutional Networks. (Training code to reproduce the original result is available.)
pytorch-segmentation-toolbox
PyTorch Implementations for DeeplabV3 and PSPNet
semantic-segmentation-pytorch
Pytorch implementation for Semantic Segmentation/Scene Parsing on MIT ADE20K dataset
vit-pytorch
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch