Neural random subspace
作者：
Highlights：
• End-to-end trainable ensemble method in a single model. We implement the random subspace method using existing CNN layers and it achieves both higher accuracy and faster inference speed than conventional forest methods. We show that such designs are attractive for many real-world tasks dealing with vector inputs.
• Efficient and effective non-linear transformation. NRS can be seamlessly installed at a CNN as a non-linear component. NRS is more efficient than previous higher-order pooling methods and achieves higher accuracy than standard GAP with negligible additional cost. We demonstrate its effectiveness on both 2D image and 3D object recognition tasks under various CNN architectures.
• Good generalization ability and transferability. The proposed method achieves better performance on various tasks when compared with different algorithms. Our method is easy to implement and can be easily extended to other deep learning libraries and deployed to different devices.
摘要
•End-to-end trainable ensemble method in a single model. We implement the random subspace method using existing CNN layers and it achieves both higher accuracy and faster inference speed than conventional forest methods. We show that such designs are attractive for many real-world tasks dealing with vector inputs.•Efficient and effective non-linear transformation. NRS can be seamlessly installed at a CNN as a non-linear component. NRS is more efficient than previous higher-order pooling methods and achieves higher accuracy than standard GAP with negligible additional cost. We demonstrate its effectiveness on both 2D image and 3D object recognition tasks under various CNN architectures.•Good generalization ability and transferability. The proposed method achieves better performance on various tasks when compared with different algorithms. Our method is easy to implement and can be easily extended to other deep learning libraries and deployed to different devices.
论文关键词：Random subspace,Ensemble learning,Deep neural networks
论文评审过程：Received 10 June 2020, Revised 10 December 2020, Accepted 18 December 2020, Available online 30 December 2020, Version of Record 6 January 2021.
论文官网地址：https://doi.org/10.1016/j.patcog.2020.107801
