Pinned Repositories
LIVECell
denoising
Code base denoising
CRF_Cell_ID
Automatic annotation of cell identities in dense cellular images.
CutGAN_easy_tensorflow
Native TensorFlow API implementation of CutGAN paper (https://arxiv.org/abs/2007.15651). This implementation may be easier to understand for beginners.
generative_models
exploring several ways of training generative models
NIDDL
Deep denoising pushes the limit of functional data acquisition by recovering high SNR calcium traces from low SNR videos acquired using low laser power or smaller exposure time. Thus deep denoising enables faster and longer volumetric recordings.
shiveshc.github.io
SinGAN_easy_tensorflow
Native TensorFlow API implementation of SinGAN paper (https://arxiv.org/pdf/1905.01164.pdf). This implementation may be easier to understand for beginners.
ViT_easy_tensorflow
Native TensorFlow API implementation of Vision Transformer paper (https://arxiv.org/pdf/2010.11929.pdf). This implementation may be easier to understand for beginners.
shiveshc's Repositories
shiveshc/NIDDL
Deep denoising pushes the limit of functional data acquisition by recovering high SNR calcium traces from low SNR videos acquired using low laser power or smaller exposure time. Thus deep denoising enables faster and longer volumetric recordings.
shiveshc/CRF_Cell_ID
Automatic annotation of cell identities in dense cellular images.
shiveshc/CutGAN_easy_tensorflow
Native TensorFlow API implementation of CutGAN paper (https://arxiv.org/abs/2007.15651). This implementation may be easier to understand for beginners.
shiveshc/generative_models
exploring several ways of training generative models
shiveshc/shiveshc.github.io
shiveshc/SinGAN_easy_tensorflow
Native TensorFlow API implementation of SinGAN paper (https://arxiv.org/pdf/1905.01164.pdf). This implementation may be easier to understand for beginners.
shiveshc/ViT_easy_tensorflow
Native TensorFlow API implementation of Vision Transformer paper (https://arxiv.org/pdf/2010.11929.pdf). This implementation may be easier to understand for beginners.