Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net论文笔记
论文主要贡献有3点：1）IN学习到的是对形状变换，比如颜色，风格，虚拟的或现实的不敏感的特征，而BN是保留纹理相关的特征；2）灵活，能够用到很多的架构；3）在迁移学习的时候不需要目标领域的数据就能够提高网络的表现。 目的是：解决输入图片的外观不变性。 方法是将IN和BN结合起来，IBN-Net。 结果是：IBN-Net能够学习捕获和消除外观的变换，而保持学到的特征的区别性。 结构特性：将IN和BN
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相关文章
- 1. 论文（5）Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
- 2. Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net阅读笔记
- 3. 论文笔记 understanding deep learning requires rethinking generalization
- 4. 论文笔记：All-at-once Optimization for Coupled Matrix and Tensor Factorizations
- 5. 论文阅读笔记：Improving Generalization via Scalable Neighborhood Component Analysis
- 6. VITAL: VIsual Tracking via Adversarial Learning论文笔记
- 7. 【论文笔记】Deep Metric Learning via Facility Location
- 8. [CVPR 2019 论文笔记] Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders
- 9. 【论文笔记】Continual Relation Learning via Episodic Memory Activation and Reconsolidation
- 10. 论文阅读笔记《Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders》