Onboard Out-of-Calibration Detection of Deep Learning Models using Conformal Prediction

Core Concepts

Conformal prediction can be used to detect if deep learning models are out-of-calibration during onboard processing.

Abstract

The paper explores the relationship between conformal prediction and model uncertainty, and exploits this relationship to perform onboard out-of-calibration detection for deep learning models.

Key highlights:

• Conformal prediction provides finite sample coverage guarantees in the form of a prediction set that is guaranteed to contain the true class within a user-defined error rate.
• The average size of the conformal prediction set is related to the uncertainty of the deep learning model. Uncertain models tend to have larger prediction sets, while overconfident models have smaller prediction sets.
• Under noisy scenarios, the outputs of uncertain models like ResNet50 become untrustworthy, leading to an increase in the average prediction set size. This can be used to detect if the model is out-of-calibration.
• Overconfident models like InceptionV3 and DenseNet161 cannot be easily detected as out-of-calibration using the prediction set size alone, as their outputs remain overconfident even under noise.
• The paper demonstrates the out-of-calibration detection procedure using popular classification models like ResNet50, DenseNet161, InceptionV3, and MobileNetV2 on the EuroSAT remote sensing dataset.

Stats

The average normalized softmax entropy increases with increasing noise severity for the ResNet50 model, indicating higher uncertainty.
The average normalized softmax entropy remains constant for the InceptionV3 and DenseNet161 models, indicating overconfidence.
The average prediction set size increases significantly for the ResNet50 model under noisy conditions, while the increase is negligible for the InceptionV3 and DenseNet161 models.

Quotes

"If exchangeability is violated, e.g., due to covariate shift [13] or noise, then (1) does not hold. How do we detect, autonomously, that a covariate shift has taken place and the model is out-of-calibration?"
"An overconfident model tends to place importance on a single class irrespective of whether the prediction is correct or not. A distribution of the softmax outputs of such a network will peak near higher values. However, if a model is uncertain, then the model tries to output a flat softmax distribution that reflects the uncertainty in its predictions."

Key Insights Distilled From

Onboard Out-of-Calibration Detection of Deep Learning Models using Conformal Prediction

by Protim Bhatt... at arxiv.org 05-07-2024

https://arxiv.org/pdf/2405.02634.pdf

Deeper Inquiries

How can the out-of-calibration detection approach be extended to other conformal prediction algorithms beyond the APS method

To extend the out-of-calibration detection approach to other conformal prediction algorithms beyond the APS method, one can leverage the fundamental principles of conformal prediction. The key lies in utilizing the model logits or output softmax to design the prediction set generation procedure and a quantile-based function to evaluate the class inclusion threshold. By incorporating these elements into different conformal prediction algorithms, one can adapt the methodology to suit the specific requirements of the model being used. Additionally, exploring how different algorithms handle uncertainty estimation and calibration can provide insights into how to integrate out-of-calibration detection effectively across various conformal prediction frameworks.

What is the impact of intrinsic model parameters, such as weight distortions, on the predictive uncertainty and out-of-calibration detection

The impact of intrinsic model parameters, such as weight distortions, on predictive uncertainty and out-of-calibration detection is significant. Weight distortions can introduce biases and inaccuracies in the model's predictions, leading to increased uncertainty in the model's outputs. This uncertainty can manifest in various ways, such as distorted softmax distributions or inconsistent prediction set sizes. When weight distortions occur, the model's ability to provide reliable predictions diminishes, making it more challenging to detect when the model is out-of-calibration. Therefore, monitoring and analyzing the effects of intrinsic model parameters on predictive uncertainty are crucial for enhancing out-of-calibration detection capabilities.

How can the out-of-calibration detection be integrated into the overall sensor system health monitoring pipeline for remote sensing applications

Integrating out-of-calibration detection into the overall sensor system health monitoring pipeline for remote sensing applications can enhance the reliability and performance of the system. By incorporating real-time monitoring of model uncertainty and calibration status, the pipeline can proactively identify when the deep learning models are out-of-calibration. This information can then trigger corrective actions or alerts to ensure the integrity of the system's outputs. Additionally, coupling out-of-calibration detection with anomaly detection techniques can provide a comprehensive approach to monitoring the health and performance of the sensor system. By continuously assessing the model's calibration and uncertainty levels, the pipeline can maintain optimal functionality and reliability in critical applications.