论文标题
NCTV：神经网络校准的神经夹具工具包和可视化
NCTV: Neural Clamping Toolkit and Visualization for Neural Network Calibration
论文作者
论文摘要
随着深度学习技术的发展，神经网络已经证明了它们在许多任务中提供准确预测的出色能力。但是，即使对于高临界模型，缺乏对神经网络校准的考虑也不会从人类那里获得信任。在这方面，必须弥合模型预测的置信度与实际正确性可能性之间的差距，以得出一个良好的模型。在本文中，我们介绍了神经夹具工具包，这是第一个开源框架，旨在帮助开发人员采用最先进的模型不稳定的校准模型。此外，我们在演示中提供动画和互动部分，以使研究人员熟悉神经网络中的校准。还引入了有关使用我们工具包的COLAB教程。
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.
