machine-learning-paper-notes

This repository contains my notes for the research papers that I read since the creation of the repository for anyone to briefly glance over the details. The papers are arranged topic-wise:

• Computer Vision
  • Change Detection
  • Semantic Segmentation
  • Weakly supervised Semantic Segmentation

Computer Vision

• Change Detection

  • Unsupervised Change Detection in Satellite Images Using Principal Component Analysis and k -Means Clustering (IEEE)
  • Automatic analysis of the difference image for unsupervised change detection (IEEE)
  • Change Detection in Satellite Images Using a Genetic Algorithm Approach (IEEE)
  • Change Detection in Synthetic Aperture Radar Images Based on Deep Neural Networks (IEEE)
  • Recurrent Neural Networks to Correct Satellite Image Classification Maps (IEEE)
  • Damage Detection from Aerial Images via Convolutional Neural Networks (IEEE)

• Semantic Segmentation

  • U-Net: Convolutional Networks for Biomedical Image Segmentation (arXiV, Code)
  • V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation(arXiV, Code)
  • Rethinking Atrous Convolution for Semantic Image Segmentation(arXiV, Code)

• Weakly supervised Semantic Segmentation

  • Learning deep structured network for weakly supervised change detection (arXiV)
  • From Image-level to Pixel-level Labeling with Convolutional Networks (arXiV, Code)
  • Weakly Supervised Semantic Segmentation Based on Web Image Co-segmentation (arXiV, Code)
  • Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation (arXiV)
  • Feedback Neural Network for Weakly Supervised Geo-Semantic Segmentation (arXiv)
  • Simple Does It: Weakly Supervised Instance and Semantic Segmentation (arXiV)