Professor Chan-Hyun Youn’s Research Team Developed a Network Calibration Technique to Improve the reliability of artificial neural networks
<(from left) Professor Chan-Hyun Youn, Gyusang Cho ph.d. candidate>
Professor Chan-Hyun Youn’s research team from the EE department, has successfully developed a network calibration algorithm called “Tilt and Average; TNA” to improve the reliability of neural networks. Unlike existing methods based on calibration maps, the TNA technique transforms the weights of the classifier’s last layer, offering a significant advantage in that it can be seamlessly integrated with existing methods. This research is being evaluated as an outstanding technology in the field of enhancing artificial intelligence reliability.
The research proposes a new algorithm to address the overconfident prediction problem inherent in existing artificial neural networks. Utilizing the high-dimensional geometry of the last linear layer, this algorithm focuses on the angular aspects between the row vectors of the weights, suggesting a mechanism to adjust (Tilt) and compute the average (Average) of their directions.
The research team confirmed that the proposed method can reduce calibration error by up to 20%, and the algorithm’s ability to integrate with existing calibration map-based techniques is a significant advantage. The results of this study are scheduled to be presented at the ICML (International Conference on Machine Learning, https://icml.cc), one of the premier international conferences in the field of artificial intelligence, held in Vienna, Austria, this July. Now in its 41st year, ICML is renowned as one of the most prestigious and long-standing international conferences in the machine learning field, alongside other top conferences such as CVPR, ICLR, and NeurIPS.
In addition, this research was conducted with support from the Korea Coast Guard (RS-2023-00238652) and the Defense Acquisition Program Administration (DAPA) (KRIT-CT-23-020). The paper can be found as : Gyusang Cho and Chan-Hyun Youn, “Tilt and Average : Geometric Adjustment of the Last Layer for Recalibration”, ICML (2024)
