NCTV: Neural Clamping Toolkit and Visualization for Neural Network Calibration


  • Lei Hsiung National Tsing Hua University IBM Research
  • Yung-Chen Tang National Tsing Hua University MediaTek Inc.
  • Pin-Yu Chen IBM Research
  • Tsung-Yi Ho National Tsing Hua University The Chinese University of Hong Kong



Neural Network Calibration, Visualization, Toolkit, Machine Learning


With 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.