# ClaudesLens: Uncertainty Quantification in Computer Vision Models

arXiv: https://arxiv.org/abs/2406.13008 · HTML: https://mlsanity.com/paper/2406.13008/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 18 Jun 2024.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [SODA10M: A Large-Scale 2D Self/Semi-Supervised Object Detection Dataset for Autonomous Driving](https://mlsanity.com/paper/2106.11118/) (2021)

## Abstract (arXiv)

In a world where more decisions are made using artificial intelligence, it is of utmost importance to ensure these decisions are well-grounded. Neural networks are the modern building blocks for artificial intelligence. Modern neural network-based computer vision models are often used for object classification tasks. Correctly classifying objects with textitcertainty has become of great importance in recent times. However, quantifying the inherent textituncertainty of the output from neural networks is a challenging task. Here we show a possible method to quantify and evaluate the uncertainty of the output of different computer vision models based on Shannon entropy. By adding perturbation of different levels, on different parts, ranging from the input to the parameters of the network, one introduces entropy to the system. By quantifying and evaluating the perturbed models on the proposed PI and PSI metrics, we can conclude that our theoretical framework can grant insight into the uncertainty of predictions of computer vision models. We believe that this theoretical framework can be applied to different applications for neural networks. We believe that Shannon entropy may eventually have a bigger role in the SOTA (State-of-the-art) methods to quantify uncertainty in artificial intelligence. One day we might be able to apply Shannon entropy to our neural systems.