# Directing DNNs Attention for Facial Attribution Classification using Gradient-weighted Class Activation Mapping

arXiv: https://arxiv.org/abs/1905.00593 · HTML: https://mlsanity.com/paper/1905.00593/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2019.

## Built on by (most cited first, 2 of 2)

- [Efficient Human-in-the-loop System for Guiding DNNs Attention](https://mlsanity.com/paper/2206.05981/) (2022)
- [Top-GAP: Integrating Size Priors in CNNs for more Interpretability, Robustness, and Bias Mitigation](https://mlsanity.com/paper/2409.04819/) (2024)

## Abstract (arXiv)

Deep neural networks (DNNs) have a high accuracy on image classification tasks. However, DNNs trained by such dataset with co-occurrence bias may rely on wrong features while making decisions for classification. It will greatly affect the transferability of pre-trained DNNs. In this paper, we propose an interactive method to direct classifiers paying attentions to the regions that are manually specified by the users, in order to mitigate the influence of co-occurrence bias. We test on CelebA dataset, the pre-trained AlexNet is fine-tuned to focus on the specific facial attributes based on the results of Grad-CAM.