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

Source: [Directing DNNs Attention for Facial Attribution Classification using Gradient-weighted Class Activation Mapping](<https://www.sota2.com/research/paper/68fa65ba1e209131cea0be0a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:33:37.375Z

Authors: Xi Yang, Bojian Wu, Issei Sato, Takeo Igarashi

Published: 2019-05-02T07:05:33.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1905.00593>)

[PDF](<https://arxiv.org/pdf/1905.00593.pdf>)