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Name: JZhang
Type: User
JZhang's Projects
act
Real-time 'Actor-Critic' tracking
amc
[ECCV 2018] AMC: AutoML for Model Compression and Acceleration on Mobile Devices
aster.pytorch
ASTER in Pytorch
benchmark_results
Visual Tracking Paper List
borderdet
Stay tuned for updates!
breakingcertifiabledefenses
Breaking Certifiable Defenses
channel-pruning
Channel Pruning for Accelerating Very Deep Neural Networks (ICCV'17)
codeclass
「编程技术学习指南」学习路线+学习资源。从编程小白到架构师，所有你需要掌握的内容都在这里
dcfst
DCFST
dynamicrouting
Learning Dynamic Routing for Semantic Segmentation
fada-pytorch
pytorch implement for the paper Few-Shot Adversarial Domain Adaptation
fasterrcnn-pytorch
FasterRCNN is implemented in VGG, ResNet and FPN base.
fcanet
Simple reimplemetation experiments about FcaNet
firsttestgithub
The first time upload the code ,this is a test code
isda-for-deep-networks
An efficient implicit semantic augmentation method, complementary to existing non-semantic techniques.
l2f
Source code for CVPR 2020 paper "Learning to Forget for Meta-Learning"
learnforspringboot
SpringBoot
learningtocompare_fsl
PyTorch code for CVPR 2018 paper: Learning to Compare: Relation Network for Few-Shot Learning (Few-Shot Learning part)
leetcode_summary
使用JavaScript刷算法题！
leetcodemy
Record my Algorithm questions
maml-pytorch
Elegant PyTorch implementation of paper Model-Agnostic Meta-Learning (MAML)
math
以现在人的努力程度之低，还谈不到拼天赋的时候
pruning_filters_for_efficient_convnets
PyTorch implementation of "Pruning Filters For Efficient ConvNets"
py-dslt
pytorch implementation of Shrinkage loss in our ECCV paper 2018: Deep regression tracking with shrinkage loss
py-faster-rcnn
Faster R-CNN (Python implementation) -- see https://github.com/ShaoqingRen/faster_rcnn for the official MATLAB version
py-mdnet
MDNet PyTorch implementation
py-vital
Vital tracker implemented using PyTorch
pycftrackers
Python re-implementation of some correlation filter based tracker
pysot-toolkit
Python Single Object Tracking Evaluation
pytorch-cnn-visualizations
Pytorch implementation of convolutional neural network visualization techniques
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