J. Keegan

Computer Vision and Pattern Recognition · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 1 researcher and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom J. Keegan trusts

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

  1. Andrew Zisserman (in Computer Vision and Pattern Recognition)

Papers from J. Keegan's perspective

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

  1. ImageNet Large Scale Visual Recognition Challenge
  2. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
  3. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
  4. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
  5. U-Net: Convolutional Networks for Biomedical Image Segmentation (International Conference on Medical Image Computing and Computer-Assisted Intervention)
  6. Image-to-Image Translation with Conditional Adversarial Networks
  7. Conditional Generative Adversarial Nets
  8. Learning Spatiotemporal Features with 3D Convolutional Networks
  9. Sparse MRI: The application of compressed sensing for rapid MR imaging
  10. Towards Principled Methods for Training Generative Adversarial Networks

Their papers