Addressing Failure Prediction by Learning Model Confidence (1910.04851v2)

Abstract: Assessing reliably the confidence of a deep neural network and predicting its failures is of primary importance for the practical deployment of these models. In this paper, we propose a new target criterion for model confidence, corresponding to the True Class Probability (TCP). We show how using the TCP is more suited than relying on the classic Maximum Class Probability (MCP). We provide in addition theoretical guarantees for TCP in the context of failure prediction. Since the true class is by essence unknown at test time, we propose to learn TCP criterion on the training set, introducing a specific learning scheme adapted to this context. Extensive experiments are conducted for validating the relevance of the proposed approach. We study various network architectures, small and large scale datasets for image classification and semantic segmentation. We show that our approach consistently outperforms several strong methods, from MCP to Bayesian uncertainty, as well as recent approaches specifically designed for failure prediction.

• The paper introduces TCP as a robust confidence criterion that outperforms traditional MCP for predicting model failures.
• It presents ConfidNet, a dedicated neural network that learns reliable confidence scores using latent features from pre-trained classifiers.
• Experimental results on datasets like CIFAR-10 and CamVid show significant AUPR-Error improvements, enhancing reliability in safety-critical applications.

Learning Model Confidence for Failure Prediction

The paper "Addressing Failure Prediction by Learning Model Confidence" presents an innovative approach to enhancing the confidence estimation capabilities of deep neural networks, primarily focusing on failure prediction. The authors propose a new confidence criterion termed True Class Probability (TCP), which markedly improves upon the conventional Maximum Class Probability (MCP) derived from softmax outputs.

Core Contributions

The primary contribution of this research lies in the introduction and validation of TCP as a confidence criterion. The TCP is argued to be more effective for failure prediction than MCP due to several theoretical and empirical advantages:

• Theoretical Guarantees: TCP provides robust theoretical guarantees for failure prediction, ensuring better separability between erroneous and correct predictions.
• Learning TCP: Since the true class is not available during inference, the authors propose a novel method for learning TCP through a dedicated neural network, ConfidNet, trained on the training dataset.

ConfidNet is designed to estimate confidence levels based on TCP, harnessing the latent features pre-trained by the classification model. This network is tasked with learning to predict a confidence score that correlates well with the likelihood of the model's prediction being correct.

Experimental Validation

The research includes extensive experiments across various datasets and network architectures, including image classification on MNIST, CIFAR-10, CIFAR-100, SVHN, as well as semantic segmentation on CamVid. The findings reveal that the proposed ConfidNet consistently outperforms several strong baseline methods such as MCP and Bayesian uncertainty, thereby validating the efficacy of TCP in failure prediction scenarios.

In quantitative terms, ConfidNet shows significant improvements in metrics like Area Under the Precision-Recall Error Curve (AUPR-Error) across different settings, indicating its superior capability to detect and predict potential prediction failures.

Practical and Theoretical Implications

The practical implications of this research are profound. In safety-critical applications—such as autonomous driving, medical diagnosis, and infrastructure monitoring—the ability to predict model failures can allow systems to defer to human judgment or auxiliary decision-making pathways, thereby preventing erroneous decisions from causing harm.

Theoretically, this work advances the understanding of confidence estimation in neural networks, particularly in high-stakes prediction contexts. By providing a mechanism to evaluate not just the prediction but the model’s certainty in cases where errors could carry significant consequences, this research opens up a pathway for more reliable AI systems.

Future Directions

Looking forward, the integration of TCP into broader AI systems presents interesting opportunities for enhancement in domains where model explainability and reliability are paramount. Moreover, exploring adversarial training techniques for generating predictive errors and refining ConfidNet's learning process could further enhance its robustness and applicability.

Ultimately, the insights gained from this work are likely to influence future research on confidence estimation in neural networks, offering a pathway toward more trustworthy AI deployments in diverse environments. The consistent performance improvements observed suggest a promising direction for future research into more advanced confidence modeling and failure prediction techniques in neural network architectures.