# TargetDrop: A Targeted Regularization Method for Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2010.10716 · HTML: https://mlsanity.com/paper/2010.10716/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2020.

## Built on by (most cited first, 3 of 3)

- [Avoiding Overfitting: A Survey on Regularization Methods for Convolutional Neural Networks](https://mlsanity.com/paper/2201.03299/) (2022)
- [AdaFSNet: Time Series Classification Based on Convolutional Network with a Adaptive and Effective Kernel Size Configuration](https://mlsanity.com/paper/2404.18246/) (2024)
- [Selective Output Smoothing Regularization: Regularize Neural Networks by Softening Output Distributions](https://mlsanity.com/paper/2103.15383/) (2021)

## Abstract (arXiv)

Dropout regularization has been widely used in deep learning but performs less effective for convolutional neural networks since the spatially correlated features allow dropped information to still flow through the networks. Some structured forms of dropout have been proposed to address this but prone to result in over or under regularization as features are dropped randomly. In this paper, we propose a targeted regularization method named TargetDrop which incorporates the attention mechanism to drop the discriminative feature units. Specifically, it masks out the target regions of the feature maps corresponding to the target channels. Experimental results compared with the other methods or applied for different networks demonstrate the regularization effect of our method.