Helmut Prosch

Computer Vision and Pattern Recognition · 2018–2020

A researcher in the Science corpus: 3 papers; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Helmut Prosch trusts

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Boris Polyak (in Numerical Methods & Scientific Computing)
  2. Kaiming He (in Computer Vision and Pattern Recognition)
  3. Shaoqing Ren (in Computer Vision and Pattern Recognition)

Papers from Helmut Prosch's perspective

Ordered by how strongly the researchers Helmut Prosch trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  2. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
  3. U-Net: Convolutional Networks for Biomedical Image Segmentation (International Conference on Medical Image Computing and Computer-Assisted Intervention)
  4. Multi-Scale Context Aggregation by Dilated Convolutions
  5. The Cityscapes Dataset for Semantic Urban Scene Understanding
  6. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
  7. Training Very Deep Networks
  8. Some methods of speeding up the convergence of iteration methods
  9. ImageNet classification with deep convolutional neural networks
  10. User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability

Their papers