kltsyn

Pinned Repositories

awesome-deep-vision
A curated list of deep learning resources for computer vision
00
Awesome-Python-Toolbox
00
batch_normalization
Batch Normalization Layer for Caffe
Language:C++00
caffe
Caffe: a fast open framework for deep learning.
Language:C++1 2 00
caffe-batchnorm
Language:C++00
caffe-stn
Spatial Transformer Network Implementation in Caffe
Language:C++00
cs231n-project
Language:Python10
DataAnalysis
Language:R10
full_stack_coder
Java后端面试资料；Leetcode；后端研发资料；各大厂架构开发学习资源；精华机器学习，NLP，图像识别等人工智能领域学习资料，搜索，推荐，广告系统架构及算法技术资料整理
20
SalientObjectDetection
Language:MATLAB10

kltsyn's Repositories

kltsyn/full_stack_coder
Java后端面试资料；Leetcode；后端研发资料；各大厂架构开发学习资源；精华机器学习，NLP，图像识别等人工智能领域学习资料，搜索，推荐，广告系统架构及算法技术资料整理
20
kltsyn/chainer
A flexible framework of neural networks for deep learning
Language:Python2 0
kltsyn/clickhouse-cli
A third-party client for the Clickhouse DBMS server.
Language:Python1 0
kltsyn/CS-Notes
:books: 技术面试必备基础知识、Leetcode、计算机操作系统、计算机网络、系统设计
kltsyn/EasyPR
An open source project for chinese plate recognition. It aims to be Easy, Flexible, and Accurate. Welcome to contribute your expertise !
Language:C++
kltsyn/FaceDetection-ConvNet-3D
Source code for our ECCV16 paper, Face Detection with End-to-End Integration of a ConvNet and a 3D Model
Language:C++
kltsyn/fashion-detection
a fashion detection framework using Fast R-CNN
Language:C++2 0
kltsyn/Generative-and-Discriminative-Voxel-Modeling
Voxel-Based Variational Autoencoders, VAE GUI, and Convnets for Classification
Language:Python2 0
kltsyn/hive
Mirror of Apache Hive
Language:Java2 0
kltsyn/incubator-airflow
Apache Airflow (Incubating)
Language:Python
kltsyn/LeetCodeAnimation
Demonstrate all the questions on LeetCode in the form of animation.（用动画的形式呈现解LeetCode题目的思路）
kltsyn/LocNet
LocNet: Improving Localization Accuracy for Object Detection
Language:Matlab
kltsyn/LogicStack-LeetCode
公众号「宫水三叶的刷题日记」刷穿 LeetCode 系列文章源码
0 0
kltsyn/marvin
Marvin: A Minimalist GPU-only N-Dimensional ConvNets Framework
Language:C++
kltsyn/mxnet
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
Language:C++
kltsyn/netscope
Neural network visualizer
Language:JavaScript2 0
kltsyn/notebooks
Google Cloud Datalab samples and documentation
Language:Jupyter Notebook
kltsyn/Pragmatic-Scala
Pragmatic Scala 中文版——《Scala实用指南》代码清单（包含 SBT 版本（切到sbt分支））
kltsyn/pure-bash-bible
📖 A collection of pure bash alternatives to external processes.
Language:Shell1 0
kltsyn/redash
Make Your Company Data Driven. Connect to any data source, easily visualize, dashboard and share your data.
kltsyn/region-proposal-network
region proposal network
Language:Python2 0
kltsyn/RGBD-object-propsal
Language:Matlab2 0
kltsyn/rgbd_detection
RGB-D detection pipeline with object proposals by EdgeBoxes and object classification by Multimodal CNN
Language:Matlab
kltsyn/Robotics-Course-project
Haze can cause poor visibility and loss of contrast in images and videos. In this article, we study the dehazing problem which can improve visibility and thus help in many computer vision applications. An extensive comparison of state of the art single image dehazing methods is done. One simple contrast enhancement method is used for dehazing. Structure- texture decomposition has been used in conjunction with this enhancement method to improve its performance in presence of synthetic noise. Methods which use a haze formation model and attempt at solving an ill-posed problem using computer vision priors are also investigated. The two priors studied are dark channel prior and the non-local prior. Both qualitative and quantitative comparisons for atmospheric and underwater images on all three methods provide a conclusive idea of which dehazing method performs better. All this knowledge has been extended to video dehazing. A video dehazing method which uses the spatial and temporal information in a video is studied in depth. An improved version of video dehazing is proposed in this article, which uses the spatial-temporal information fusion framework but does not suffer from some of its limitations. The new video dehazing method is shown to produce better results on test videos
Language:Matlab
kltsyn/SceneNetv1.0
Still a work in progress and adding code..
Language:C++
kltsyn/scrap-cvpr2016
Language:Python
kltsyn/sql-training
kltsyn/superset
Superset is a data exploration platform designed to be visual, intuitive, and interactive
Language:Python
kltsyn/treeNode
TreeNode Test
kltsyn/winutils
winutils.exe hadoop.dll and hdfs.dll binaries for hadoop windows
Language:Shell0 0