NCTV: Neural Clamping Toolkit and Visualization for Neural Network Calibration
Keywords:Neural Network Calibration, Visualization, Toolkit, Machine Learning
AbstractWith the advancement of deep learning technology, neural networks have demonstrated their excellent ability to provide accurate predictions in many tasks. However, a lack of consideration for neural network calibration will not gain trust from humans, even for high-accuracy models. In this regard, the gap between the confidence of the model's predictions and the actual correctness likelihood must be bridged to derive a well-calibrated model. In this paper, we introduce the Neural Clamping Toolkit, the first open-source framework designed to help developers employ state-of-the-art model-agnostic calibrated models. Furthermore, we provide animations and interactive sections in the demonstration to familiarize researchers with calibration in neural networks. A Colab tutorial on utilizing our toolkit is also introduced.
How to Cite
Hsiung, L., Tang, Y.-C., Chen, P.-Y., & Ho, T.-Y. (2023). NCTV: Neural Clamping Toolkit and Visualization for Neural Network Calibration. Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16446-16448. https://doi.org/10.1609/aaai.v37i13.27074