ROS 시작하기(Getting Started with ROS:: Your First Steps in Robot Programming )Hansol Kang
This document provides an introduction to ROS (Robot Operating System) and instructions for getting started. It discusses key ROS concepts like packages, nodes, messages, topics, services and actions. It also provides guides for installing ROS1 and ROS2 on Ubuntu, WSL2 or using Docker. Finally, it covers using URDF files to define robot models and RViz for visualization.
Photo-realistic Single Image Super-resolution using a Generative Adversarial ...Hansol Kang
* Ledig, Christian, et al. "Photo-realistic single image super-resolution using a generative adversarial network." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
LSGAN - SIMPle(Simple Idea Meaningful Performance Level up)Hansol Kang
LSGAN은 기존의 GAN loss가 아닌 MSE loss를 사용하여, 더욱 realistic한 데이터를 생성함.
LSGAN 논문 리뷰 및 PyTorch 기반의 구현.
[참고]
Mao, Xudong, et al. "Least squares generative adversarial networks." Proceedings of the IEEE International Conference on Computer Vision. 2017.
InfoGAN : Interpretable Representation Learning by Information Maximizing Gen...Hansol Kang
InfoGAN은 기존 GAN이 manupulation이 어렵다는 단점을 극복함. latent space에 z 이외에 c(condition)을 부여하여 원하는 결과물을 얻을 수 있음. c에 대해 잘 학습하기 위해 Mutual information을 이용해 상관관계를 부여함.
InfoGAN 논문 리뷰 및 PyTorch 기반의 구현.
my github : https://github.com/messy-snail/GAN_PyTorch
[참고]
https://github.com/taeoh-kim/Pytorch_InfoGAN
Chen, Xi, et al. "Infogan: Interpretable representation learning by information maximizing generative adversarial nets." Advances in neural information processing systems. 2016.
딥러닝 중급 - AlexNet과 VggNet (Basic of DCNN : AlexNet and VggNet)Hansol Kang
The document summarizes the basics of Deep Convolutional Neural Networks (DCNNs) including AlexNet and VGGNet. It discusses how AlexNet introduced improvements like ReLU activation and dropout to address overfitting issues. It then focuses on the VGGNet, noting that it achieved good performance through increasing depth using small 3x3 filters and adding convolutional layers. The document shares details of VGGNet configurations ranging from 11 to 19 weight layers and their performance on image classification tasks.
PyTorch is an open-source machine learning library for Python. It is primarily developed by Facebook's AI research group. The document discusses setting up PyTorch, including installing necessary packages and configuring development environments. It also provides examples of core PyTorch concepts like tensors, common datasets, and constructing basic neural networks.
Deep Convolutional GANs - meaning of latent spaceHansol Kang
DCGAN은 GAN에 단순히 conv net을 적용했을 뿐만 아니라, latent space에서도 의미를 찾음.
DCGAN 논문 리뷰 및 PyTorch 기반의 구현.
VAE 세미나 이슈 사항에 대한 리뷰.
my github : https://github.com/messy-snail/GAN_PyTorch
[참고]
https://github.com/znxlwm/pytorch-MNIST-CelebA-GAN-DCGAN
https://github.com/taeoh-kim/Pytorch_DCGAN
Radford, Alec, Luke Metz, and Soumith Chintala. "Unsupervised representation learning with deep convolutional generative adversarial networks." arXiv preprint arXiv:1511.06434 (2015).
쉽게 설명하는 GAN (What is this? Gum? It's GAN.)Hansol Kang
The document discusses generative adversarial networks (GANs). It begins with an introduction to GANs, describing their concept and training process. It then reviews a seminal GAN paper, discussing its mathematical formulation of GAN training as a minimax game and theoretical results showing global optimality can be achieved. The document concludes by outlining the configuration, implementation, and flowchart for a GAN experiment.
신뢰 전파 기법을 이용한 스테레오 정합(Stereo matching using belief propagation algorithm)Hansol Kang
스테레오 정합, 신뢰 전파 기법에 대한 개념과 간단한 예제.
[참고]
J.H. Kim, and Y.H. Ko, “Multibaseline based Stereo Matching Using Texture adaptive Belief Propagation Technique." Journal of the Institute of Electronics and Information Engineers Vol. 50, No. 1, pp.75-85, 2013.
QT 프로그래밍 기초(basic of QT programming tutorial)Hansol Kang
This document provides an introduction and tutorial on Qt, an open source cross-platform application framework. It discusses installing Qt, creating a basic Qt application, and using various Qt widgets like buttons, line edits, radio buttons, check boxes, combo boxes, graphics views and more. Code examples are provided to demonstrate how to use these widgets to build a image processing application in Qt that can load, display, and manipulate images.
This document discusses simulation of a mobile robot. It includes the following sections:
1. Introduction of the Pioneer 3-DX mobile robot and goals of the simulation project.
2. Dynamics and kinematics equations for the robot, including equations of motion.
3. A Simulink block diagram for PD control of the robot. Equations for calculating joint accelerations from velocities are also presented.
딥러닝 기초 - XOR 문제와 딥뉴럴넷(Basic of DL - XOR problem and DNN)Hansol Kang
The document provides an introduction to deep neural networks (DNNs) and discusses some of the key challenges in training them, such as the XOR problem, vanishing gradients, and proper weight initialization. It then presents an implementation of a DNN for classifying MNIST handwritten digits using TensorFlow that applies techniques like ReLU activation, Xavier/He initialization, and dropout to improve performance. The model achieves over 90% accuracy on the test data after 15 epochs of training.
ROS 시작하기(Getting Started with ROS:: Your First Steps in Robot Programming )Hansol Kang
This document provides an introduction to ROS (Robot Operating System) and instructions for getting started. It discusses key ROS concepts like packages, nodes, messages, topics, services and actions. It also provides guides for installing ROS1 and ROS2 on Ubuntu, WSL2 or using Docker. Finally, it covers using URDF files to define robot models and RViz for visualization.
Photo-realistic Single Image Super-resolution using a Generative Adversarial ...Hansol Kang
* Ledig, Christian, et al. "Photo-realistic single image super-resolution using a generative adversarial network." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
LSGAN - SIMPle(Simple Idea Meaningful Performance Level up)Hansol Kang
LSGAN은 기존의 GAN loss가 아닌 MSE loss를 사용하여, 더욱 realistic한 데이터를 생성함.
LSGAN 논문 리뷰 및 PyTorch 기반의 구현.
[참고]
Mao, Xudong, et al. "Least squares generative adversarial networks." Proceedings of the IEEE International Conference on Computer Vision. 2017.
InfoGAN : Interpretable Representation Learning by Information Maximizing Gen...Hansol Kang
InfoGAN은 기존 GAN이 manupulation이 어렵다는 단점을 극복함. latent space에 z 이외에 c(condition)을 부여하여 원하는 결과물을 얻을 수 있음. c에 대해 잘 학습하기 위해 Mutual information을 이용해 상관관계를 부여함.
InfoGAN 논문 리뷰 및 PyTorch 기반의 구현.
my github : https://github.com/messy-snail/GAN_PyTorch
[참고]
https://github.com/taeoh-kim/Pytorch_InfoGAN
Chen, Xi, et al. "Infogan: Interpretable representation learning by information maximizing generative adversarial nets." Advances in neural information processing systems. 2016.
딥러닝 중급 - AlexNet과 VggNet (Basic of DCNN : AlexNet and VggNet)Hansol Kang
The document summarizes the basics of Deep Convolutional Neural Networks (DCNNs) including AlexNet and VGGNet. It discusses how AlexNet introduced improvements like ReLU activation and dropout to address overfitting issues. It then focuses on the VGGNet, noting that it achieved good performance through increasing depth using small 3x3 filters and adding convolutional layers. The document shares details of VGGNet configurations ranging from 11 to 19 weight layers and their performance on image classification tasks.
PyTorch is an open-source machine learning library for Python. It is primarily developed by Facebook's AI research group. The document discusses setting up PyTorch, including installing necessary packages and configuring development environments. It also provides examples of core PyTorch concepts like tensors, common datasets, and constructing basic neural networks.
Deep Convolutional GANs - meaning of latent spaceHansol Kang
DCGAN은 GAN에 단순히 conv net을 적용했을 뿐만 아니라, latent space에서도 의미를 찾음.
DCGAN 논문 리뷰 및 PyTorch 기반의 구현.
VAE 세미나 이슈 사항에 대한 리뷰.
my github : https://github.com/messy-snail/GAN_PyTorch
[참고]
https://github.com/znxlwm/pytorch-MNIST-CelebA-GAN-DCGAN
https://github.com/taeoh-kim/Pytorch_DCGAN
Radford, Alec, Luke Metz, and Soumith Chintala. "Unsupervised representation learning with deep convolutional generative adversarial networks." arXiv preprint arXiv:1511.06434 (2015).
쉽게 설명하는 GAN (What is this? Gum? It's GAN.)Hansol Kang
The document discusses generative adversarial networks (GANs). It begins with an introduction to GANs, describing their concept and training process. It then reviews a seminal GAN paper, discussing its mathematical formulation of GAN training as a minimax game and theoretical results showing global optimality can be achieved. The document concludes by outlining the configuration, implementation, and flowchart for a GAN experiment.
신뢰 전파 기법을 이용한 스테레오 정합(Stereo matching using belief propagation algorithm)Hansol Kang
스테레오 정합, 신뢰 전파 기법에 대한 개념과 간단한 예제.
[참고]
J.H. Kim, and Y.H. Ko, “Multibaseline based Stereo Matching Using Texture adaptive Belief Propagation Technique." Journal of the Institute of Electronics and Information Engineers Vol. 50, No. 1, pp.75-85, 2013.
QT 프로그래밍 기초(basic of QT programming tutorial)Hansol Kang
This document provides an introduction and tutorial on Qt, an open source cross-platform application framework. It discusses installing Qt, creating a basic Qt application, and using various Qt widgets like buttons, line edits, radio buttons, check boxes, combo boxes, graphics views and more. Code examples are provided to demonstrate how to use these widgets to build a image processing application in Qt that can load, display, and manipulate images.
This document discusses simulation of a mobile robot. It includes the following sections:
1. Introduction of the Pioneer 3-DX mobile robot and goals of the simulation project.
2. Dynamics and kinematics equations for the robot, including equations of motion.
3. A Simulink block diagram for PD control of the robot. Equations for calculating joint accelerations from velocities are also presented.
딥러닝 기초 - XOR 문제와 딥뉴럴넷(Basic of DL - XOR problem and DNN)Hansol Kang
The document provides an introduction to deep neural networks (DNNs) and discusses some of the key challenges in training them, such as the XOR problem, vanishing gradients, and proper weight initialization. It then presents an implementation of a DNN for classifying MNIST handwritten digits using TensorFlow that applies techniques like ReLU activation, Xavier/He initialization, and dropout to improve performance. The model achieves over 90% accuracy on the test data after 15 epochs of training.
3. Introduction
Detection의 종류
2019-07-15
3
Detection
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• Region proposal and detection 구조
• 속도는 느리나 정확도가 높음
• eg. R-CNN 계열
R-FCN(Region-based Fully Convolutional Networks)은 R-CNN 계열에 FCN 구조를 접목함.
12. R-FCN
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2019-07-15
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2019-07-15
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