Exploration into techniques for colorizing images with deep neural networks
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Updated
Feb 8, 2017 - Python
Exploration into techniques for colorizing images with deep neural networks
🎨 Automatic Image Colorization using TensorFlow based on Residual Encoder Network
For use together with the python3 logging module: Allows to automatically color logging messages based on their level.
Final version of the CNN project. Works with balloon, cat, cars and human faces.
Image colorization with a Multivariate Bernoulli Mixture Density network.
This is the implementation of the "Comicolorization: Semi-automatic Manga Colorization"
U-Net Model conditioned with MobileNet features for Grayscale -> Color mapping
Grayscale Image Colorization using deep CNN and Inception-ResNet-v2 (DD2424 Deep Learning in Science course at KTH 2017)
Automatic icon colorization using deep convolutional neural networks. "Towards Icon Design Using Machine Learning." In Stanford CS229, Fall 2017.
Pytorch implementation of "Pixcolor:Pixel Recursive Colorization" (in progress)
Train an FCN segmentation network from scratch on the self-supervised task of colorizing grayscale images.
U-Net Model conditioned with MobileNet features for Grayscale -> Color mapping
Reconstruction full-pol data from single-pol SAR data
Utilize deep learning models to automatically colorize grayscale images
Easily print in color to the python console, on any platform, without external modules.
Convolutional Neural Network for Colorization of Images w/ PyTorch
This is a keras implementation of paper Colorful Image Colorization.
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