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advmcdenoise

anttweakbar

Development fork from kypp/anttweakbar. Unless I have changes kypp hasn't merged yet, his is the canonical copy.

blender

Official mirror of Blender

bmfr

Blockwise Multi-Order Feature Regression for Real-Time Path Tracing Reconstruction

caffe-deep-shading

Modified Caffe for Deep Shading.

codinginterviewchinese2

《剑指Offer》第二版源代码（Clone from: https://github.com/zhedahht/CodingInterviewChinese2）

cs231n_spring_2017_assignment

My implementations of cs231n 2017

cuda-qr

A new QR decomposition algorithm implemented in CUDA

dcgan-for-bird-generation

DCGAN and WGAN implementation on Keras for Bird Generation

deepillumination

Code and examples from our paper "Deep Illumination: Approximating Dynamic Global Illumination with Generative Adversarial Networks," by Manu Mathew Thomas and Angus Forbes

deeplearning_tutorials

The deeplearning algorithms implemented by tensorflow

dl_jie_pan

Image classification using CNN and DBN

dls-example

Introduction of usage deep learning service of huawei cloud

fast-non-local-means-and-asymptotic-non-local-means

Non-Local means denoising (NLM) algorithm is a milestone algorithm in the field of image processing. The proposal of NLM has opened up the non-local method which has a deep influence. This paper performed a revisit for NLM from two aspects as follows: 1. To alleviate the high computational complexity problem of NLM, a fast algorithm was constructed, which was based on cross-correlation and fast Fourier transform; 2. NLM always blur structures and textures during the noise removal, especially in the case of strong noise. To solve this problem, an Asymptotic Non-Local Means image denoising algorithm is put forward, which uses the property of noise variance to control the filtering parameters. Numerical experiments illustrate that the fast algorithm is 27 times faster than classical implementation with standard parameter configuration, and the ANLM uniformly outperforms classical NLM, in terms of both PSNR and visual effects.

gans-collections-tf2.0_keras-eager_mode

This repository implements all kinds of GAN-models based on tensorflow2.0 keras API including GAN, CGAN, WGAN, WGAN_GP, VAE, CVAE, LSGAN, infoGAN, EBGAN, BEGAN, ACGAN

gans-keras

GANs Implementations in Keras

generative_adversarial_networks_101

Keras implementations of Generative Adversarial Networks. GAN, DCGAN, CGAN, CCGAN, WGAN and LSGAN models with MNIST and CIFAR-10 datasets.

glm

OpenGL Mathematics (GLM)

gltf

glTF – Runtime 3D Asset Delivery

gltf-sample-models

glTF Sample Models

gravityengine

my dx12 playground

keras

Deep Learning library for Python. Runs on TensorFlow, Theano, or CNTK.

keras-mnist-tutorial

For a mini tutorial at U of T, a tutorial on MNIST classification in Keras.

keras-resnet

Residual networks implementation using Keras-1.0 functional API

layerdenoise

Neural Denoising with Layer Embeddings

lighthouse2

Lighthouse 2 framework for real-time ray tracing

lstm-mnist

RNN classifier built with Keras to classify MNIST dataset

lstm-music-genre-classification

Music genre classification with LSTM Recurrent Neural Nets

machine-learning

my machine-learning tutorial

minimal_wgan

A minimal implementation of Wasserstein GAN