Pinned Repositories
acai
Code for "Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer"
AdvBox
Advbox is a toolbox to generate adversarial examples that fool neural networks in PaddlePaddle、PyTorch、Caffe2、MxNet、Keras、TensorFlow and Advbox can benchmark the robustness of machine learning models. Advbox give a command line tool to generate adversarial examples with Zero-Coding.
Adversarial-Transformation-Network
A simple implement of an Adversarial Autoencoding ATN(AAE ATN)
arbitrary_style_transfer
Fast Neural Style Transfer with Arbitrary Style using AdaIN Layer - Based on Huang et al. "Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization"
DMTK
Microsoft Distributed Machine Learning Toolkit
examples
A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
federated
A framework for implementing federated learning
graph_nets
Build Graph Nets in Tensorflow
midrae
Code for paper Improving Representation Learning in Autoencoders via Multidimensional Interpolation and Dual Regularizations.
mixing-generator-data
ZeitgeistQIAN's Repositories
ZeitgeistQIAN/federated
A framework for implementing federated learning
ZeitgeistQIAN/APDrawingGAN
Code for APDrawingGAN: Generating Artistic Portrait Drawings from Face Photos with Hierarchical GANs (CVPR 2019 Oral)
ZeitgeistQIAN/awesome-automl-papers
A curated list of automated machine learning papers, articles, tutorials, slides and projects
ZeitgeistQIAN/CartoonGan-tensorflow
Generate your own cartoon-style images with CartoonGAN (CVPR 2018), powered by TensorFlow 2.0 Alpha.
ZeitgeistQIAN/cdp
Code for our ECCV 2018 work.
ZeitgeistQIAN/dabnn
dabnn is an accelerated binary neural networks inference framework for mobile platform
ZeitgeistQIAN/DALI
A library containing both highly optimized building blocks and an execution engine for data pre-processing in deep learning applications
ZeitgeistQIAN/DCGAN-LSGAN-WGAN-GP-DRAGAN-Tensorflow-2
DCGAN LSGAN WGAN-GP DRAGAN Tensorflow 2
ZeitgeistQIAN/deeplearning-models
A collection of various deep learning architectures, models, and tips
ZeitgeistQIAN/distiller
Neural Network Distiller by Intel AI Lab: a Python package for neural network compression research. https://nervanasystems.github.io/distiller
ZeitgeistQIAN/docs
TensorFlow documentation
ZeitgeistQIAN/DPSR
Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels (CVPR, 2019) (PyTorch)
ZeitgeistQIAN/ENAS-pytorch
PyTorch implementation of "Efficient Neural Architecture Search via Parameters Sharing"
ZeitgeistQIAN/flutter
Flutter makes it easy and fast to build beautiful mobile apps.
ZeitgeistQIAN/GCNet
GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond
ZeitgeistQIAN/interpretable-ml-book
Book about interpretable machine learning
ZeitgeistQIAN/layer-rotation-paper-experiments
Code for reproducing results of the paper "Layer rotation: a surprisingly powerful indicator of generalization in deep networks?"
ZeitgeistQIAN/mmsr
Open MMLab Image and Video Super-Resolution Toolbox, , including SRResNet, SRGAN, ESRGAN, EDVR, etc.
ZeitgeistQIAN/probability
Probabilistic reasoning and statistical analysis in TensorFlow
ZeitgeistQIAN/pumpkin-book
《机器学习》(西瓜书)公式推导解析,在线阅读地址:https://datawhalechina.github.io/pumpkin-book
ZeitgeistQIAN/SAN
Second-order Attention Network for Single Image Super-resolution (CVPR-2019)
ZeitgeistQIAN/serving
A flexible, high-performance serving system for machine learning models
ZeitgeistQIAN/sonnet
TensorFlow-based neural network library
ZeitgeistQIAN/SRFBN_CVPR19
Pytorch code for our paper "Feedback Network for Image Super-Resolution" (CVPR2019)
ZeitgeistQIAN/tensorboard
TensorFlow's Visualization Toolkit
ZeitgeistQIAN/tensorflow
An Open Source Machine Learning Framework for Everyone
ZeitgeistQIAN/tensorwatch
Debugging, monitoring and visualization for Python Machine Learning and Data Science
ZeitgeistQIAN/tfx
TFX is an end-to-end platform for deploying production ML pipelines
ZeitgeistQIAN/tpu
Reference models and tools for Cloud TPUs.
ZeitgeistQIAN/Versatile-Filters
Pytorch code for paper: Learning Versatile Filters for Efficient Convolutional Neural Networks (NeurIPS 2018)