《Deep Active Learning for Axon-Myelin Segmentation on Histology Data》--阅读笔记-Arxiv

时间 2021-01-02

标签 active learning MCDropout 繁體版

原文原文链接

Active learning的核心是选择most informative的sample，这个策略是有创新型的（https://github.com/neuropoly/deep-active-learning） Authors M´elanie Lubrano di Scandalea, Christian S. Perone, Mathieu Boudreau, Julien Cohen

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1. 《A sparse annotation strategy based on attention-guided active learning for 3D medic》--阅读笔记-Arxiv
2. 阅读论文：Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation
3. Deep Watershed Transform for Instance Segmentation阅读笔记
4. A Review on Deep Learning Techniques Applied to Semantic Segmentation 阅读笔记
5. 论文阅读笔记《Deep Active Learning for Civil Infrastructure Defect Detection and Classification》
6. 论文阅读笔记《Segmentation-based deep-learning approach for surface-defect detection》
7. 【论文阅读笔记】PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
8. 论文阅读：PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
9. Deep Residual Learning for Image Recognition阅读笔记
10. Deep Reinforcement Learning for Dialogue Generation阅读笔记

>>更多相关文章<<