Double_V (LDOUBLEV)

LDOUBLEV

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Company:huazhong univisity of science and technology

Location:wuhan,hubei

Home Page:https://blog.csdn.net/qq_25737169

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Double_V's repositories

semi-supervised-GAN

There is the semi-supervised implement of 'Improved Techniques for Training GANs '

Machine-Learning-Tutorials

machine learning and deep learning tutorials, articles and other resources

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BEGAN-tensorflow

python3 / Implementation of Google Brain's BEGAN in Tensorflow

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DCGAN-tensorflow

A tensorflow implementation of "Deep Convolutional Generative Adversarial Networks"

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DeepLearningBookQA_cn

深度学习面试问题 回答对应的DeepLearning中文版页码

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keras-dcgan

Keras implementation of Deep Convolutional Generative Adversarial Networks

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lihang_book_algorithm

致力于将李航博士《统计学习方法》一书中所有算法实现一遍

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Classification_Nets

Implement popular models by different DL framework. Such as tensorflow and caffe

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Results

results

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AdvSemiSeg

Pytorch implementation of the paper "Adversarial Learning for Semi-supervised Semantic Segmentation," Wei-Chih Hung, Yi-Hsuan Tsai, Yan-Ting Liou, Yen-Yu Lin, and Ming-Hsuan Yang

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awesome-object-detection

Awesome Object Detection based on handong1587 github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html

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cnn_finetune

Fine-tune CNN in Keras

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DeepLearningFrameworks

Demo of running NNs across different frameworks

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FaderNetworks

Fader Networks: Manipulating Images by Sliding Attributes - NIPS 2017

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GP-GAN

Official Chainer implementation of GP-GAN: Towards Realistic High-Resolution Image Blending

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magenta

Magenta: Music and Art Generation with Machine Intelligence

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markdown-here

Google Chrome, Firefox, and Thunderbird extension that lets you write email in Markdown and render it before sending.

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Mask_RCNN

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

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pytorch-generative-model-collections

Collection of generative models in Pytorch version.

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README

README文件语法解读,即Github Flavored Markdown语法介绍

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weightnorm

Example code for Weight Normalization, from "Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks"

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