Generative Adversarial Network for single image super-resolution in high content screening microscopy images
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Updated
Jan 20, 2018 - Jupyter Notebook
Generative Adversarial Network for single image super-resolution in high content screening microscopy images
implement Deep Feature Consisten Variational Autoencoder in Tensorflow
Low-dose CT via Transfer Learning from a 2D Trained Network, In IEEE TMI 2018
The implementation code of Thesis project which entitled "Photo-to-Emoji Transformation with TraVeLGAN and Perceptual Loss" as a final project in my master study.
Pytorch Implementation of Hou, Shen, Sun, Qiu, "Deep Feature Consistent Variational Autoencoder", 2016
A VGG-based perceptual loss function for PyTorch.
Comparing different similarity functions for reconstruction of image on CycleGAN. (https://tandon-a.github.io/CycleGAN_ssim/) Training cycleGAN with different loss functions to improve visual quality of produced images
A simple and minimalistic implementation of the fast neural style transfer method presented in "Perceptual Losses for Real-Time Style Transfer and Super-Resolution" by Johnson et. al. (2016) 🏞
A deep perceptual metric for 3D point clouds
Demos of neural image editing
Experiments with perceptual loss and autoencoders.
Investigation in 4x Super-resolution by Deep Convolutional Neural Networks
LPIPS metric on PaddlePaddle. pip install paddle-lpips
StyleGAN Encoder - converts real images to latent space
A Study of Deep Perceptual Metrics for Image quality Assessment
Final assignment in the NLP course at the Technion (IEM097215). In this assignment we propose a novel architecture to handle both Text-to-Image translation and Image-to-Text translation tasks on paired data, using a unified architecture of transformers and CNNs and enforcing cycle consistency.
Demake-up Filter Use Unet model, Resnet50 Pretrained
Implementation of the fast neural style transfer algorithm on Keras. Includes Jupyter notebooks, python script and web app.
A perceptual weighting filter loss for DNN training in speech enhancement
🎨 Implementation of Fast Neural Style Transfer proposed by Justin Johnson et al. in the paper Perceptual Losses for Real-Time Style Transfer and Super-Resolution
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