Pinned Repositories
ACMR
reproduce the results of Adversarial Cross-Modal retrieval (ACMR)
ACMR-demo
basic modal for cross-modal-retrieval
Canonical-Correlation-and-its-Variants
This repository has the code for the Canonical Correlation and its Variants : found at the paper "Cluster Canonical Correlation Analysis"
CCA-images-text
Canonical Correlation Analysis for joint representations of images and tags
CCA-images-text-1
Classifying_Cancer_ResNet18_Pytorch
Cpp_Primer_Practice
搞定C++:punch:。C++ Primer 中文版第5版学习仓库，包括笔记和课后练习答案。
Cross-Modal-Retrieval
Cross-Modal Retrieval, triplet loss, Pytorch, Resnet18, Bert, Deep Hashing
crossmodal
Python code for the cross-modal retrieval system proposed at ACM MM '10 in "A New Approach to Cross-Modal Multimedia Retrieval"
cutlass
CUDA Templates for Linear Algebra Subroutines
shhn1's Repositories
shhn1/ACMR
reproduce the results of Adversarial Cross-Modal retrieval (ACMR)
shhn1/ACMR-demo
basic modal for cross-modal-retrieval
shhn1/CCA-images-text
Canonical Correlation Analysis for joint representations of images and tags
shhn1/CCA-images-text-1
shhn1/Cpp_Primer_Practice
搞定C++:punch:。C++ Primer 中文版第5版学习仓库，包括笔记和课后练习答案。
shhn1/Cross-Modal-Retrieval
Cross-Modal Retrieval, triplet loss, Pytorch, Resnet18, Bert, Deep Hashing
shhn1/cutlass
CUDA Templates for Linear Algebra Subroutines
shhn1/DeepLearning-500-questions
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scutjy2015@163.com 版权所有，违权必究 Tan 2018.06
shhn1/DogsVsCats-ResNet18
use torchvision models ResNet18 to implement Kaggle's dogs vs. cats task
shhn1/fashion-mnist
A MNIST-like fashion product database. Benchmark :point_right:
shhn1/Games
Some games created by python code.
shhn1/GASA
Fine-Grained Cross-Modal Retrieval based on generative and adversarial network
shhn1/gitignore
A collection of useful .gitignore templates
shhn1/image_classification_pytorch
Pytorch实践简单， 方便， 快速训练 图像分类模型
shhn1/MHTN_TCYB2018
Source code of our IEEE TCYB 2018 paper "MHTN: Modal-adversarial Hybrid Transfer Network for Cross-modal Retrieval"
shhn1/mica-deep-mcca
Deep Multiset Canonical Correlation Analysis - An extension of CCA to multiple datasets
shhn1/mine
shhn1/MINE-1
my MINE implementation [DV, fGAN, infoNCE]
shhn1/mmdetection
OpenMMLab Detection Toolbox and Benchmark
shhn1/myRep
shhn1/PyKCCA
Python implementation of Kernel Canonical Correlation Analysis
shhn1/pyrcca
Regularized kernel canonical correlation analysis in Python
shhn1/pytorch-cifar-models
3.41% and 17.11% error on CIFAR-10 and CIFAR-100
shhn1/pytorch-image-classification
use pytorch to do image classfiication tasks
shhn1/pytorch-mask-rcnn
shhn1/pytorch_image_classification
PyTorch implementation of image classification models for CIFAR-10/CIFAR-100/MNIST/FashionMNIST/Kuzushiji-MNIST/ImageNet
shhn1/TextTopicNet
Self-supervised learning of visual features through embedding images into text topic spaces
shhn1/TextTopicNet_pytorch
shhn1/torch-residual-networks
This is a Torch implementation of ["Deep Residual Learning for Image Recognition",Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun](http://arxiv.org/abs/1512.03385) the winners of the 2015 ILSVRC and COCO challenges.
shhn1/wsabie
Provide preprocessed labels of NUS-WIDE dataset in numpy format