Making Better Mistakes: Leveraging Class Hierarchies with Deep Networks (1912.09393v2)

Abstract: Deep neural networks have improved image classification dramatically over the past decade, but have done so by focusing on performance measures that treat all classes other than the ground truth as equally wrong. This has led to a situation in which mistakes are less likely to be made than before, but are equally likely to be absurd or catastrophic when they do occur. Past works have recognised and tried to address this issue of mistake severity, often by using graph distances in class hierarchies, but this has largely been neglected since the advent of the current deep learning era in computer vision. In this paper, we aim to renew interest in this problem by reviewing past approaches and proposing two simple modifications of the cross-entropy loss which outperform the prior art under several metrics on two large datasets with complex class hierarchies: tieredImageNet and iNaturalist'19.

• The paper introduces HXE, a novel loss function that incorporates hierarchical relationships to penalize semantically severe misclassifications.
• The authors employ soft labels to embed class similarities, enabling the network to reflect semantic distances in error handling.
• Evaluation on tieredImageNet and iNaturalist’19 demonstrates improved trade-offs between traditional accuracy metrics and reducing critical misclassification errors.

Leveraging Class Hierarchies in Deep Neural Network Image Classification

The paper "Making Better Mistakes: Leveraging Class Hierarchies with Deep Networks," proposes methodologies for structuring image classification errors in a semantically meaningful way using class hierarchies. The authors address the conventional practice in deep neural networks where all incorrect classifications are considered equally erroneous. Through this investigation, the paper aims to provide improved approaches to mitigate the severity of mistakes by employing class hierarchies, with a focus on two large datasets: tieredImageNet and iNaturalist'19.

Overview of Methods

1. Hierarchical Cross-Entropy (HXE): One method introduced is HXE, which incorporates hierarchical information directly into the loss function. The approach involves factorizing class probabilities in terms of conditional probabilities along paths in a hierarchy tree and weighting these conditions to penalize mistakes accordingly. The implementation is straightforward, allowing seamless integration into existing architectures through simple reweighting of standard cross-entropy loss components.
2. Soft Labels: Another approach is the use of soft labels, where class relationships are embedded by modifying label distributions in a hierarchical context. Instead of one-hot vectors, classes are represented by probability mass functions influenced by distances within the class hierarchy. These embeds capture semantic confusion and manage uncertainty reflecting how humans might mistakenly categorize visually similar classes.

Evaluation and Results

The paper assesses the proposed methods against traditional cross-entropy loss, as well as other hierarchy-based methods like DeViSE and YOLOv2. Findings demonstrate that by varying hyperparameters, both HXE and soft labels can effectively trade-off between minimizing top-k errors and reducing hierarchical mistake severity. Notably, soft labels show more adaptability in leveraging hierarchical distances for classification refinement, particularly with larger value sets, where severe misclassifications are more common. These results emphasize the tension between improved error semantics and traditional accuracy metrics.

Implications and Future Directions

This research underscores the importance of considering class hierarchy in image classification tasks. The methodologies proposed highlight practical implications, such as enhancing autonomous systems' reliability by preventing severe misclassifications that might lead to catastrophic outcomes. Theoretical discourse is also advanced by illustrating the trade-offs between robustness and conventional accuracy, suggesting emerging intersections with adversarial robustness debates.

Looking forward, the paper sets a basis for further exploration into the balance between hierarchy-informed classification strategies and their practical applications across diverse domains. Potential developments in AI could leverage these hierarchical insights to refine learning from smaller, more semantically structured datasets, ultimately driving improvements in both generalization and performance precision.