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movie0587's Projects

gan icon gan

Resources and Implementations of Generative Adversarial Nets: GAN, DCGAN, WGAN, CGAN, InfoGAN

gumbel icon gumbel

Gumbel-Softmax Variational Autoencoder with Keras

gumbel-softmax icon gumbel-softmax

categorical variational autoencoder using the Gumbel-Softmax estimator

http-server icon http-server

a simple zero-configuration command-line http server

imgaug icon imgaug

Image augmentation for machine learning experiments.

keras-cn icon keras-cn

Chinese keras documents with more examples, explanations and tips.

keras-gan icon keras-gan

Keras implementations of Generative Adversarial Networks.

keras-oneshot icon keras-oneshot

koch et al, Siamese Networks for one-shot learning, (mostly) reimplimented in keras

local-texture-descriptors icon local-texture-descriptors

Matlab implementation, comparision and improvement of Local texture descriptors. This repo demonstrate usage of Local binary pattern (LBP), Local derivative pattern (LDP), Local Tetra pattern (LTrP), Noise Resistant LBP (NR-LBP), Histogram Refinement of Local texture descriptor for Content based image retrieval (CBIR) application.

mask_rcnn icon mask_rcnn

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

models icon models

Models and examples built with TensorFlow

nsfw-resnet icon nsfw-resnet

NSFW implement in pytorch(色情图&性感图识别,本程序经过了线上大数据集测试,性能优异效果良好)

nsfw_data_scrapper icon nsfw_data_scrapper

Collection of scripts to aggregate image data for the purposes of training an NSFW Image Classifier

numpycnn icon numpycnn

Convolutional neural network implementation using NumPy

omniglot icon omniglot

Omniglot data set for one-shot learning

palm-print-recognition icon palm-print-recognition

A palm-print recognition method, called CompCode, is provided. This method is firstly proposed in paper Competitive Coding Scheme for Palm-print Verification (ICPR'04). In addition, we provide improved versions of CompCode, called M-CompCode, E-CompCode, and ME-CompCode. For more details, please refer to our paper ....

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