Dear all,
you are cordially invited to join the master's thesis
defense of Shamim Miroliaei tomorrow at 12:00 in room 05.013 (CogSys Lab).
Shamim Miroliaei will defend her thesis Exploring Potential Biases in Sequential Data Classification for Hand Gesture Recognition using Layer-wise Relevance Propagation and Cluster Analysis
Abstract:
In recent years, significant improvements have been made in Machine Learning (ML) based models. Primarily, improving these models aims to increase their learning rate. However, the functionality of ML, especially models with Convolutional Neural Network (CNN) architecture, is still complex for humans to understand. A significant method to explore the intricacies of the CNN model is to analyze the models with Layer-wise Relevance Propagation (LRP). This research examines the model’s performance trained on sequential data extracted from the hand gesture recognition dataset. To understand the model’s performance and possible biases, gender-based comparisons were made on the analysis results with different LRP rules, and then the results were clustered. Various biases were revealed by examining the LRP methods and clustering of the relevance maps generated via LRP. Detected biases are classified as sampling bias (for example, recognizing a face, accessory, or sleeve when detecting hand gestures), omitted variable bias (inability to see the entire hand), and representation bias (finger movements and incorrect hand angles that cause fuzzy hand gesture recognition), and algorithmic bias (overlap detection in detected clusters that cause misinterpreted results). Interpreting the results of the bias in the dataset emphasizes the importance of choosing LRP rules and using appropriate clustering methods. The obtained results can be used to reduce any obvious discriminatory tendencies in the CNN model. It is essential to acknowledge the inherent limitations of the dataset, particularly its inadequacy in covering all potential hand gestures.