NCTV: Neural Clamping Toolkit and Visualization for Neural Network Calibration
This addresses the issue of trust in high-accuracy neural networks for developers and researchers by providing tools to improve calibration, though it is incremental as it builds on existing calibration methods.
The paper tackles the problem of neural network calibration by introducing the Neural Clamping Toolkit, an open-source framework that helps developers use state-of-the-art model-agnostic calibrated models, with results including animations, interactive demonstrations, and a Colab tutorial to aid researchers.
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.