chrisbyd Goto Github PK
Name: 陈勇彪
Type: User
Company: Shanghai Jiaotong University
Bio: Creepy and erratic Ph.D in SJTU. Body builder= =
Location: Shanghai
Name: 陈勇彪
Type: User
Company: Shanghai Jiaotong University
Bio: Creepy and erratic Ph.D in SJTU. Body builder= =
Location: Shanghai
My own cross modal hashing baseline for research
Learning Compact Binary Descriptors with Unsupervised Deep Neural Networks (CVPR16)
The implementation of CVPR-17 paper "Deep Visual-Semantic Quantization of Efficient Image Retrieval"
cvpr2020/cvpr2019/cvpr2018/cvpr2017 papers,极市团队整理
The official source code for the paper Consensus-Aware Visual-Semantic Embedding for Image-Text Matching (ECCV 2020)
Unsupervised Domain Adaptation Papers and Code
source code for "Deep adversarial discrete hashing for cross-modal retrieval"
Domain agnostic learning with disentangled representations
Deep Adversarial Metric Learning
An implement of our paper “DEEP ADVERSARIAL QUANTIZATION NETWORK FOR CROSS-MODAL RETRIEVAL”
A PyTorch toolbox for domain adaptation and semi-supervised learning.
source code for paper "Deep Cross-Modal Hashing"
Deep learning cross modal hashing in PyTorch
Deep Cross-Modal Projection Learning for Image-Text Matching
Experiment about Deep Person Re-identification with EfficientNet-v2
End-to-end learning of deep visual representations for image retrieval
深度学习面试宝典(含数学、机器学习、深度学习、计算机视觉、自然语言处理和SLAM等方向)
Papers and Codes about Deep Metric Learning/Deep Embedding
Code for papers "Hashing with Mutual Information" (TPAMI 2019) and "Hashing with Binary Matrix Pursuit" (ECCV 2018)
Torchreid: Deep learning person re-identification in PyTorch.
pytorch implementation of Deep Supervised Hashing
Implementation of accepted AAAI paper: Deep Unsupervised Image Hashing by Maximizing Bit Entropy
An Open-Source Package for Deep Learning to Hash (DeepHash)
Must-read papers on deep learning to hash (DeepHash)
Implementation of Some Deep Hash Algorithms, Including DPSH、DSH、DHN、HashNet、DSDH、DTSH、DFH、GreedyHash、CSQ.
Learning deep representations by mutual information estimation and maximization
深度学习500问,以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述,以帮助自己及有需要的读者。 全书分为18个章节,50余万字。由于水平有限,书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作,联系[email protected] 版权所有,违权必究 Tan 2018.06
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