論文（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，獲得了冠軍，mIoU達到86.18% motivation 由於數據集衆多，導致當前的CNN模型基本上只適用於某一個domain或者task，如何在不fine-tuning的情況
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相關文章
- 1. Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net論文筆記
- 2. Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net閱讀筆記
- 3. 『 論文閱讀』Understanding deep learning requires rethinking generalization
- 4. 編碼原則 之 Once and Only Once
- 5. 論文筆記 understanding deep learning requires rethinking generalization
- 6. 論文筆記：All-at-once Optimization for Coupled Matrix and Tensor Factorizations
- 7. 《DLOW:Domain Flow for Adaptation and Generalization》論文解析
- 8. 論文閱讀筆記：Improving Generalization via Scalable Neighborhood Component Analysis
- 9. 論文-《Towards Optimal Binary Code Learning via Ordinal Embedding》
- 10. [CVPR 2019 論文筆記] Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders