All Projects → thuml → Hashnet

thuml / Hashnet

Licence: mit
Code release for "HashNet: Deep Learning to Hash by Continuation" (ICCV 2017)

Projects that are alternatives of or similar to Hashnet

Personal
Contains Jupyter Notebooks of stuff I am working on.
Stars: ✭ 190 (-1.04%)
Mutual labels:  jupyter-notebook
Magic
MAGIC (Markov Affinity-based Graph Imputation of Cells), is a method for imputing missing values restoring structure of large biological datasets.
Stars: ✭ 189 (-1.56%)
Mutual labels:  jupyter-notebook
Simpleselfattention
A simpler version of the self-attention layer from SAGAN, and some image classification results.
Stars: ✭ 192 (+0%)
Mutual labels:  jupyter-notebook
Deep Learning Paper Review And Practice
꼼꼼한 딥러닝 논문 리뷰와 코드 실습
Stars: ✭ 184 (-4.17%)
Mutual labels:  jupyter-notebook
Feature Engineering
Stars: ✭ 191 (-0.52%)
Mutual labels:  jupyter-notebook
Vanillacnn
Implementation of the Vanilla CNN described in the paper: Yue Wu and Tal Hassner, "Facial Landmark Detection with Tweaked Convolutional Neural Networks", arXiv preprint arXiv:1511.04031, 12 Nov. 2015. See project page for more information about this project. http://www.openu.ac.il/home/hassner/projects/tcnn_landmarks/ Written by Ishay Tubi : ishay2b [at] gmail [dot] com https://www.l
Stars: ✭ 191 (-0.52%)
Mutual labels:  jupyter-notebook
Adversarialvariationalbayes
This repository contains the code to reproduce the core results from the paper "Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks".
Stars: ✭ 190 (-1.04%)
Mutual labels:  jupyter-notebook
Deep Learning Random Explore
Stars: ✭ 192 (+0%)
Mutual labels:  jupyter-notebook
Teachopencadd
TeachOpenCADD: a teaching platform for computer-aided drug design (CADD) using open source packages and data
Stars: ✭ 190 (-1.04%)
Mutual labels:  jupyter-notebook
Trajectron Plus Plus
Code accompanying the ECCV 2020 paper "Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data" by Tim Salzmann*, Boris Ivanovic*, Punarjay Chakravarty, and Marco Pavone (* denotes equal contribution).
Stars: ✭ 191 (-0.52%)
Mutual labels:  jupyter-notebook
Deep Learning Notes
My personal notes, presentations, and notebooks on everything Deep Learning.
Stars: ✭ 191 (-0.52%)
Mutual labels:  jupyter-notebook
Statistical Learning Method Camp
统计学习方法训练营课程作业及答案,视频笔记在线阅读地址:https://relph1119.github.io/statistical-learning-method-camp
Stars: ✭ 191 (-0.52%)
Mutual labels:  jupyter-notebook
Activitynet 2016 Cvprw
Tools to participate in the ActivityNet Challenge 2016 (NIPSW 2016)
Stars: ✭ 191 (-0.52%)
Mutual labels:  jupyter-notebook
Cnn Re Tf
Convolutional Neural Network for Multi-label Multi-instance Relation Extraction in Tensorflow
Stars: ✭ 190 (-1.04%)
Mutual labels:  jupyter-notebook
Ml Tutorial
Introduction to ML packages for the 6.86x course
Stars: ✭ 189 (-1.56%)
Mutual labels:  jupyter-notebook
Bet On Sibyl
Machine Learning Model for Sport Predictions (Football, Basketball, Baseball, Hockey, Soccer & Tennis)
Stars: ✭ 190 (-1.04%)
Mutual labels:  jupyter-notebook
Pydata Cookbook
PyData Cookbook Project
Stars: ✭ 191 (-0.52%)
Mutual labels:  jupyter-notebook
Research2vec
Representing research papers as vectors / latent representations.
Stars: ✭ 192 (+0%)
Mutual labels:  jupyter-notebook
Facenet
FaceNet for face recognition using pytorch
Stars: ✭ 192 (+0%)
Mutual labels:  jupyter-notebook
Cl Jupyter
An enhanced interactive Shell for Common Lisp (based on the Jupyter protocol)
Stars: ✭ 191 (-0.52%)
Mutual labels:  jupyter-notebook

HashNet

HashNet Library

This is the code release for "HashNet: Deep Learning to Hash by Continuation" (ICCV 2017)

The caffe version is in directory "caffe".

The pytorch version is in directory "pytorch". We have released the version test on PyTorch Version 0.3.1.

Thank @soon-will for contributing the 'pairwise_loss_updated' with weight in pytorch version.

Citation

If you use this code for your research, please consider citing:

    @article{cao2017hashnet,
      title={HashNet: Deep Learning to Hash by Continuation},
      author={Cao, Zhangjie and Long, Mingsheng and Wang, Jianmin and Yu, Philip S},
      journal={arXiv preprint arXiv:1702.00758},
      year={2017}
    }

Contact

If you have any problem about our code, feel free to contact

or describe your problem in Issues.

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].