Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net閱讀筆記
Paper：https://arxiv.org/abs/1807.09441 Code：https://github.com/XingangPan/IBN-Net 文章摘要：卷積神經網絡(CNNs)在許多計算機視覺問題上取得了巨大的成功。與現有爲提高某一場景的性能設計的CNN不同，論文的IBN-Net顯著增強了CNN的建模能力，並且具有較強的泛化能力，可以不經過netuning即可在另一場景取得不
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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. 論文（5）Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
- 3. 論文閱讀筆記：Improving Generalization via Scalable Neighborhood Component Analysis
- 4. VITAL: Visual Tracking via Adversarial Learning 閱讀筆記
- 5. 《Causal Learning and Explanation of Deep Neural Networks via Autoencoded Activations》閱讀筆記
- 6. 論文閱讀筆記《Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders》
- 7. 論文閱讀筆記《One-Shot Visual Imitation Learning via Meta-Learning》
- 8. 『 論文閱讀』Understanding deep learning requires rethinking generalization
- 9. Machine learning Year閱讀筆記
- 10. Fuzzing File Systems via Two-Dimensional Input Space Exploration 論文閱讀筆記