luonango's Stars
DeepRec-AI/HybridBackend
A high-performance framework for training wide-and-deep recommender systems on heterogeneous cluster
PersiaML/PERSIA
High performance distributed framework for training deep learning recommendation models based on PyTorch.
shenweichen/DeepCTR
Easy-to-use,Modular and Extendible package of deep-learning based CTR models .
tensorflow/recommenders-addons
Additional utils and helpers to extend TensorFlow when build recommendation systems, contributed and maintained by SIG Recommenders.
BaguaSys/bagua
Bagua Speeds up PyTorch
kwai/DouZero
[ICML 2021] DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning | 斗地主AI
Mikoto10032/AutomaticWeightedLoss
Multi-task learning using uncertainty to weigh losses for scene geometry and semantics, Auxiliary Tasks in Multi-task Learning
hongleizhang/RSPapers
A Curated List of Must-read Papers on Recommender System.
RUCAIBox/RecBole
A unified, comprehensive and efficient recommendation library
NOBLES5E/bincode-grpc
Rust high performance RPC based on gRPC without protobuf :)
HarisIqbal88/PlotNeuralNet
Latex code for making neural networks diagrams
wzhe06/Ad-papers
Papers on Computational Advertising
vandit15/Class-balanced-loss-pytorch
Pytorch implementation of the paper "Class-Balanced Loss Based on Effective Number of Samples"
labuladong/fucking-algorithm
刷算法全靠套路，认准 labuladong 就够了！English version supported! Crack LeetCode, not only how, but also why.
wyharveychen/CloserLookFewShot
source code to ICLR'19, 'A Closer Look at Few-shot Classification'
apache/eventmesh
EventMesh is a new generation serverless event middleware for building distributed event-driven applications.
fendouai/PyTorchDocs
PyTorch 官方中文教程包含 60 分钟快速入门教程，强化教程，计算机视觉，自然语言处理，生成对抗网络，强化学习。欢迎 Star，Fork！
hzwer/shareOI
算法竞赛课件分享
ZJULearning/AttentionZSL
Codes for Paper "Attribute Attention for Semantic Disambiguation in Zero-Shot Learning"
thunlp/GNNPapers
Must-read papers on graph neural networks (GNN)
sbharadwajj/awesome-zero-shot-learning
A curated list of papers, code and resources pertaining to zero shot learning
IcewineChen/mxnet-batch_hard_triplet_loss
mxnet version batch hard triplet loss
huanghoujing/person-reid-triplet-loss-baseline
Rank-1 89% (Single Query) on Market1501 with raw triplet loss, In Defense of the Triplet Loss for Person Re-Identification, using Pytorch
THUFutureLab/gluon-face
An unofficial Gluon FR Toolkit for face recognition. https://gluon-face.readthedocs.io
996icu/996.ICU
Repo for counting stars and contributing. Press F to pay respect to glorious developers.
rafaelpadilla/Object-Detection-Metrics
Most popular metrics used to evaluate object detection algorithms.
zhreshold/ICCV19-GluonCV
Tutorial Materials for ICCV19
D-X-Y/NAS-Bench-201
NAS-Bench-201 API and Instruction
USTCPCS/CVPR2018_attention
Context Encoding for Semantic Segmentation MegaDepth: Learning Single-View Depth Prediction from Internet Photos LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume On the Robustness of Semantic Segmentation Models to Adversarial Attacks SPLATNet: Sparse Lattice Networks for Point Cloud Processing Left-Right Comparative Recurrent Model for Stereo Matching Enhancing the Spatial Resolution of Stereo Images using a Parallax Prior Unsupervised CCA Discovering Point Lights with Intensity Distance Fields CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation Learning a Discriminative Feature Network for Semantic Segmentation Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Semantic Segmentation Unsupervised Deep Generative Adversarial Hashing Network Monocular Relative Depth Perception with Web Stereo Data Supervision Single Image Reflection Separation with Perceptual Losses Zoom and Learn: Generalizing Deep Stereo Matching to Novel Domains EPINET: A Fully-Convolutional Neural Network for Light Field Depth Estimation by Using Epipolar Geometry FoldingNet: Interpretable Unsupervised Learning on 3D Point Clouds Decorrelated Batch Normalization Unsupervised Learning of Depth and Egomotion from Monocular Video Using 3D Geometric Constraints PU-Net: Point Cloud Upsampling Network Real-Time Monocular Depth Estimation using Synthetic Data with Domain Adaptation via Image Style Transfer Tell Me Where To Look: Guided Attention Inference Network Residual Dense Network for Image Super-Resolution Reflection Removal for Large-Scale 3D Point Clouds PlaneNet: Piece-wise Planar Reconstruction from a Single RGB Image Fully Convolutional Adaptation Networks for Semantic Segmentation CRRN: Multi-Scale Guided Concurrent Reflection Removal Network DenseASPP: Densely Connected Networks for Semantic Segmentation SGAN: An Alternative Training of Generative Adversarial Networks Multi-Agent Diverse Generative Adversarial Networks Robust Depth Estimation from Auto Bracketed Images AdaDepth: Unsupervised Content Congruent Adaptation for Depth Estimation DeepMVS: Learning Multi-View Stereopsis GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation Single-Image Depth Estimation Based on Fourier Domain Analysis Single View Stereo Matching Pyramid Stereo Matching Network A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation Image Correction via Deep Reciprocating HDR Transformation Occlusion Aware Unsupervised Learning of Optical Flow PAD-Net: Multi-Tasks Guided Prediciton-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing Surface Networks Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation TextureGAN: Controlling Deep Image Synthesis with Texture Patches Aperture Supervision for Monocular Depth Estimation Two-Stream Convolutional Networks for Dynamic Texture Synthesis Unsupervised Learning of Single View Depth Estimation and Visual Odometry with Deep Feature Reconstruction Left/Right Asymmetric Layer Skippable Networks Learning to See in the Dark
Cadene/pretrained-models.pytorch
Pretrained ConvNets for pytorch: NASNet, ResNeXt, ResNet, InceptionV4, InceptionResnetV2, Xception, DPN, etc.