NCTV: Neural Clamping Toolkit and Visualization for Neural Network Calibration

TL;DR

This paper introduces NCTV, an open-source toolkit for calibrating neural networks, with visualization tools and tutorials to help developers improve model confidence alignment and trustworthiness.

Contribution

The paper presents the first comprehensive toolkit for neural network calibration, including visualization and interactive features to aid research and development.

Findings

Provides a user-friendly toolkit for neural network calibration

Includes visualization and interactive features for better understanding

Offers a Colab tutorial for easy adoption

Abstract

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.