Prediction Accuracy & Reliability: Classification and Object Localization under Distribution Shift (2409.03543v1)

Abstract: Natural distribution shift causes a deterioration in the perception performance of convolutional neural networks (CNNs). This comprehensive analysis for real-world traffic data addresses: 1) investigating the effect of natural distribution shift and weather augmentations on both detection quality and confidence estimation, 2) evaluating model performance for both classification and object localization, and 3) benchmarking two common uncertainty quantification methods - Ensembles and different variants of Monte-Carlo (MC) Dropout - under natural and close-to-natural distribution shift. For this purpose, a novel dataset has been curated from publicly available autonomous driving datasets. The in-distribution (ID) data is based on cutouts of a single object, for which both class and bounding box annotations are available. The six distribution-shift datasets cover adverse weather scenarios, simulated rain and fog, corner cases, and out-of-distribution data. A granular analysis of CNNs under distribution shift allows to quantize the impact of different types of shifts on both, task performance and confidence estimation: ConvNeXt-Tiny is more robust than EfficientNet-B0; heavy rain degrades classification stronger than localization, contrary to heavy fog; integrating MC-Dropout into selected layers only has the potential to enhance task performance and confidence estimation, whereby the identification of these layers depends on the type of distribution shift and the considered task.

Evaluation of CNN Robustness and Uncertainty Quantification under Distributional Shift

The paper "Prediction Accuracy & Reliability: Classification and Object Localization under Distribution Shift" presents a meticulous exploration of how convolutional neural networks (CNNs) handle distribution shifts in the context of traffic-related data. This paper is particularly pertinent given the operational demands of autonomous driving systems, where unpredictable environmental conditions and varying data distributions are common.

Objectives and Methodology

The research primarily focuses on three areas:

1. Effect of Distribution Shift and Weather Augmentations: The investigation quantifies the effect of natural distribution shifts, including adverse weather conditions, on detection quality and confidence estimation of CNNs.
2. Model Performance Evaluation: The paper assesses models' performance in both classification and object localization tasks, providing a comprehensive understanding of CNN robustness under varied testing environments.
3. Benchmarking Uncertainty Quantification Methods: Two main uncertainty estimation techniques—Ensembles and Monte-Carlo (MC) Dropout variants—are explored under distribution shifts using a curated dataset, AD-Cifar-7, derived from publicly available autonomous driving datasets.

Dataset and Experimental Details

The AD-Cifar-7 dataset is a novel creation from existing datasets like BDD100K, NuScenes, KITTI, and CADC. It contains a diverse range of real-world traffic scenarios, including different weather conditions (clear, overcast, rain, fog, and snow) and corner cases. The dataset is used to simulate distribution shifts, thereby enabling a comprehensive evaluation of CNN behavior under these conditions.

Three CNN architectures—ResNet-50, EfficientNet-B0, and ConvNeXt-Tiny—are compared. ConvNeXt-Tiny is found to be the most robust among the architectures, showing lower performance variation and more robustness across the board.

Key Findings

1. Impact of Distribution Shift: The paper confirms that CNNs' performance degrades notably under distribution shifts. Severe drops in task performance were observed during conditions like heavy rain and fog, with accuracies falling below 80% in some cases.
2. Uncertainty Quantification Robustness: Ensembles consistently enhance classification accuracy and robustness in confidence estimation across different settings of distribution shifts, serving as the benchmark for uncertainty. In several cases, MC-Dropout, particularly its more computationally efficient variants, like Head-Dropout or After-BB-Dropout, maintains competitive performance with Ensembles, offering a feasible trade-off between accuracy and computational cost.
3. Feature Representation and Task Dependence: The research highlights that the robustness of UQ methods depends on the granularity level of feature representations they target. For instance, classification benefits more from targeting high-level-feature representations, while object localization is more reliant on object-level representations.
4. Implications for Real-world Scenarios: The findings emphasize the need for enhanced robustness of neural networks deployed in autonomous systems, especially under adverse weather conditions and distribution shifts that could compromise safety-critical operations.

Implications and Future Work

The implications of this work extend to the development of more resilient and reliable AI systems, particularly for applications like autonomous driving. The granular approach taken in this paper reveals potential pathways for optimizing uncertainty estimation by tailoring methods to expected types of distribution shifts specific to application tasks.

Future developments in AI could further refine these approaches, exploring advanced architectures and methodologies to bolster model robustness and ensure dependable performance despite the inherent variability in real-world data.

In conclusion, this research offers a significant contribution to understanding and mitigating the impact of distribution shifts on model performance in practical, safety-critical applications. By linking the type of expected distribution shift with appropriate uncertainty quantification methods, the paper encourages more strategic deployment of CNNs in challenging operational environments.