Predicting strong gravitational lens wavelength information in JWST NIRcam imaging as observed by Euclid VIS/NISP
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Updated
Feb 9, 2024 - Jupyter Notebook
Predicting strong gravitational lens wavelength information in JWST NIRcam imaging as observed by Euclid VIS/NISP
PyTorch implementation of 'Pix2Pix' (Isola et al., 2017) and training it on Facades and Google Maps
Keras implementations of Generative Adversarial Networks. GANs, DCGAN, CGAN, CCGAN, WGAN and LSGAN models with MNIST and CIFAR-10 datasets.
Efficient Subsampling of Realistic Images From GANs Conditional on a Class or a Continuous Variable
The mel spectrogram generator using conditional WGAN-GP. For the mel spectrogram inverter, look up HiFi-GAN
Using a GAN to synthetically generate medical images for DL purposes
SAGAN that conducted a CT noise reduction study based on conditional GAN
Ancient coins reconstruction using CGANs
Using pix2pix and SinGAN to get into the movie
Source code and pretrained models for pix2pix - Inference on image and paint using pyqt5
Conditional Generative Adversarial Network for generating synthetic faces with user specified attributes
PANDA (Pytorch) pipeline, is a computational toolbox (MATLAB + pytorch) for generating PET navigators using Generative Adversarial networks.
Conditional Generative Adversarial Networks(cgans) to convert text to image implemented in Python and TensorFlow & Keras
TensorFlow implementation of Conditional Generative Adversarial Nets (CGAN) with MNIST dataset.
A Tensorflow 2 implementation of SNGAN and Projection Discriminator
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