eaglezjh

eaglezjh's Stars

• FanChiMao/HWMNet

  Half Wavelet Attention on M-Net+ for Low-light Image Enhancement

  Language:Python9815

• qilong-zhang/Patch-wise-iterative-attack

  Patch-wise iterative attack (accepted by ECCV 2020) to improve the transferability of adversarial examples.

  Language:Python9020

• ghuan99/CCDistill

  9

• WZMIAOMIAO/deep-learning-for-image-processing

  deep learning for image processing including classification and object-detection etc.

  Language:Python24.5k8.2k

• aim-uofa/AdelaiDepth

  This repo contains the projects: 'Virtual Normal', 'DiverseDepth', and '3D Scene Shape'. They aim to solve the monocular depth estimation, 3D scene reconstruction from single image problems.

  Language:Python1.1k148

• AmingWu/VDD-DAOD

  Vector-Decomposed Disentanglement for Domain-Invariant Object Detection

  Language:Python315

• xdxuyang/SelfSAGCN

  SelfSAGCN: Self-Supervised Semantic Alignment for Graph Convolution Network

  Language:Python121

• salesforce/PCL

  PyTorch code for "Prototypical Contrastive Learning of Unsupervised Representations"

  Language:Python58283

• Leo-Q-316/ImGAGN

  Imbalanced Network Embedding vi aGenerative Adversarial Graph Networks

  Language:Python278

• cyqclark/fedlearn-algo

  Fedlearn支持前沿算法研发的Python工具库 | Fedlearn algorithm toolkit for researchers

  Language:Python9341

• justjavac/free-programming-books-zh_CN

  :books: 免费的计算机编程类中文书籍，欢迎投稿

  113k28.3k

• mims-harvard/GNNGuard

  Defending graph neural networks against adversarial attacks (NeurIPS 2020)

  Language:Python6415

• askforalfred/alfred

  ALFRED - A Benchmark for Interpreting Grounded Instructions for Everyday Tasks

  Language:C41488

• thunlp/GNNPapers

  Must-read papers on graph neural networks (GNN)

  16.3k3k

• yalesong/tvsum

  TVSum: Title-based Video Summarization dataset (CVPR 2015)

  Language:MATLAB12713

• INTERMT/Awesome-PyTorch-Chinese

  【干货】史上最全的PyTorch学习资源汇总

  Language:Python4.4k828

• fengdu78/deeplearning_ai_books

  deeplearning.ai（吴恩达老师的深度学习课程笔记及资源）

  Language:HTML19k6k