论文（5）Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net 在WAD 2018的Drivable Area track 的task中，获得了冠军，mIoU达到86.18% motivation 由于数据集众多，导致当前的CNN模型基本上只适用于某一个domain或者task，如何在不fine-tuning的情况
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相关文章
- 1. Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net论文笔记
- 2. Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net阅读笔记
- 3. 『 论文阅读』Understanding deep learning requires rethinking generalization
- 4. 编码原则 之 Once and Only Once
- 5. 论文笔记 understanding deep learning requires rethinking generalization
- 6. 论文笔记：All-at-once Optimization for Coupled Matrix and Tensor Factorizations
- 7. 《DLOW:Domain Flow for Adaptation and Generalization》论文解析
- 8. 论文阅读笔记：Improving Generalization via Scalable Neighborhood Component Analysis
- 9. 论文-《Towards Optimal Binary Code Learning via Ordinal Embedding》
- 10. [CVPR 2019 论文笔记] Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders