稀缺与无约束环境下的航空图像分类：基于共形预测
Aerial Image Classification in Scarce and Unconstrained Environments via Conformal Prediction
AI总结：
本文在稀缺且无约束的航空图像数据集上实证评估共形预测，发现即使数据有限，该方法也能提供可靠的置信度估计，并指出温度缩放并非总有益，模型压缩具有部署潜力。
摘要：
本文对共形预测方法在一个具有挑战性的航空图像数据集上进行了全面的实证分析，该数据集包含无约束环境中的多样化事件。共形预测是一种强大的后处理技术，它接受任何分类器的输出并将其转换为一个可能的标签集合，为真实标签的覆盖提供统计保证。与在标准基准上的评估不同，我们的研究解决了数据稀缺和高变异性现实环境中的复杂性。我们研究了利用预训练模型（MobileNet、DenseNet和ResNet）在有限标注数据上微调以生成信息性预测集的有效性。为了进一步评估校准的影响，我们考虑了两条并行流水线（有和没有温度缩放），并使用两个关键指标评估性能：经验覆盖率和平均预测集大小。这一设置使我们能够系统地检查校准选择如何影响可靠性和效率之间的权衡。我们的研究结果表明，即使使用相对较小的标注样本和简单的非一致性分数，共形预测也能为复杂任务提供有价值的置信度估计。此外，我们的分析揭示，虽然温度缩放常用于校准，但它并不总是导致更小的预测集，这强调了在其应用中仔细考虑的重要性。进一步地，我们的结果突出了模型压缩技术在共形预测流水线中用于资源受限环境部署的巨大潜力。基于我们的观察，我们主张未来研究应深入探讨噪声或模糊标签对共形预测性能的影响，并探索有效的模型缩减策略。
英文摘要：
This paper presents a comprehensive empirical analysis of conformal prediction methods on a challenging aerial image dataset featuring diverse events in unconstrained environments. Conformal prediction is a powerful post-hoc technique that takes the output of any classifier and transforms it into a set of likely labels, providing a statistical guarantee on the coverage of the true label. Unlike evaluations on standard benchmarks, our study addresses the complexities of data-scarce and highly variable real-world settings. We investigate the effectiveness of leveraging pretrained models (MobileNet, DenseNet, and ResNet), fine-tuned with limited labeled data, to generate informative prediction sets. To further evaluate the impact of calibration, we consider two parallel pipelines (with and without temperature scaling) and assess performance using two key metrics: empirical coverage and average prediction set size. This setup allows us to systematically examine how calibration choices influence the trade-off between reliability and efficiency. Our findings demonstrate that even with relatively small labeled samples and simple nonconformity scores, conformal prediction can yield valuable uncertainty estimates for complex tasks. Moreover, our analysis reveals that while temperature scaling is often employed for calibration, it does not consistently lead to smaller prediction sets, underscoring the importance of careful consideration in its application. Furthermore, our results highlight the significant potential of model compression techniques within the conformal prediction pipeline for deployment in resource-constrained environments. Based on our observations, we advocate for future research to delve into the impact of noisy or ambiguous labels on conformal prediction performance and to explore effective model reduction strategies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
