Directing DNNs Attention for Facial Attribution Classification using Gradient-weighted Class Activation Mapping
This addresses the issue of transferability in pre-trained DNNs for facial attribution classification, but it is incremental as it builds on existing techniques like Grad-CAM.
The paper tackles the problem of deep neural networks relying on wrong features due to co-occurrence bias in image classification, proposing an interactive method to direct classifiers to user-specified regions, which improved focus on specific facial attributes in tests on the CelebA dataset.
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.