Not logged in.
Quick Search - Contribution
Contribution Details
| Type | Bachelor's Thesis |
| Scope | Discipline-based scholarship |
| Title | Visualization of Facial Attribute Classifiers via Class Activation Mapping |
| Organization Unit | |
| Authors |
|
| Supervisors |
|
| Language |
|
| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2023 |
| Abstract Text | The use of convolutional neural networks (CNNs) in image classification tasks is a rapidly progressing field of research, including the classification of facial attributes. However, it is not yet completely understood how CNNs make decisions. To improve the transparency of the decision-making process and thus enhance interpretability and trustworthiness of CNNs, methods have been developed to visualize this process. In this thesis, we use the Gradient-weighted Class Activation Mapping (Grad-CAM) technique proposed by Selvaraju et al. (2017) to identify the regions of an image that the CNN uses for classification. This technique produces class-specific heatmaps that are intuitively interpretable. In order to evaluate the class activation maps, we define a set of masks, one for each of the 40 facial attributes that we examine. By using an approach called Acceptable Mask Ratio (AMR) we quantify how much of the activated area lies within the masked area. The higher the value of the AMR the more active is the CNN within the area that we expect, which usually corresponds to the location of the attribute being classified. We compare two different CNNs, one considers the class imbalance inherent to the data set (balanced CNN), and the other does not (unbalanced CNN). Our results show that overall the balanced CNN more often uses image regions that lie within the masked area. Furthermore, the results show an unexpected pattern for the unbalanced CNN namely for highly biased attributes the Grad-CAMs for the majority class show no activity at all. |
| PDF File |
Download
|
| Export |
BibTeX
|