# NCTV: Neural Clamping Toolkit and Visualization for Neural Network Calibration

arXiv: https://arxiv.org/abs/2211.16274 · HTML: https://mlsanity.com/paper/2211.16274/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 10 references that are in the map.
- First posted on arXiv 29 Nov 2022.

## Builds on (most cited first, 6 of 6)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration](https://mlsanity.com/paper/1910.12656/) (2019)
- [Calibration of Neural Networks using Splines](https://mlsanity.com/paper/2006.12800/) (2020)
- [Improved Trainable Calibration Method for Neural Networks on Medical Imaging Classification](https://mlsanity.com/paper/2009.04057/) (2020)
- [A Geometric Perspective towards Neural Calibration via Sensitivity Decomposition](https://mlsanity.com/paper/2110.14577/) (2021)
- [Neural Clamping: Joint Input Perturbation and Temperature Scaling for Neural Network Calibration](https://mlsanity.com/paper/2209.11604/) (2022)

## Built on by (most cited first, 2 of 2)

- [AutoVP: An Automated Visual Prompting Framework and Benchmark](https://mlsanity.com/paper/2310.08381/) (2023)
- [When Does Visual Prompting Outperform Linear Probing for Vision-Language Models? A Likelihood Perspective](https://mlsanity.com/paper/2409.01821/) (2024)

## Abstract (arXiv)

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.