[MICCAI2019] Overfitting of neural nets under class imbalance: Analysis and improvements for segment

时间 2021-01-12

标签 MICCAI2019 overfitting 繁體版

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作者单位： Zeju Li，Biomedical Image Analysis Group, Imperial College London 分析在前景比较少（类别不平衡）的分割场景中，逐步减少训练数据量，作者发现precision稳定，sensitivity下降明显。作者进一步可视化分类层的输出，发现1、前景离决策面太近，背景离决策面较远；2、随训练数据量减少，前景train和test的均值

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1. A Taxonomy of Deep Convolutional Neural Nets for Computer Vision
2. Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts
3. Neural Networks and Deep Learning -- Class 3: Shallow neural networks
4. The 101 of ELF files on Linux: Understanding and Analysis
5. LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks
6. Fine-tuning Convolutional Neural Networks for Biomedical Image Analysis: Actively and Incrementally
7. New in 10.2.2: C++ and Debugger Improvements
8. Neural Networks and Deep Learning -- Class 2: Neural Network Basics
9. 论文笔记-An Analysis of Deep Neural Network Models for Practical Applications
10. Regularization - The problem of overfitting

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