提出少样本下评估模型在图像失真下的可用性方法
Few-shot Algorithm Assurance
- 用水平集估计法构建分类规则，判断模型在失真下的准确率是否达标
- 在五个数据集上显著优于基线方法，少样本场景下仍保持高精度
- 适合关注模型鲁棒性验证的工程师和研究者
在图像分类任务中，深度学习模型易受图像失真影响。为确保模型可部署，需识别其准确率仍高于阈值的失真程度，这一问题称为图像失真下的模型保证。本文将其建模为分类任务：给定失真程度，预测模型在失真图像上的准确率是否超过阈值。提出一种基于水平集估计（LSE）算法的新型分类器，利用LSE的均值与方差函数构建分类规则。进一步拓展至少样本场景，仅依赖少量真实图像时，通过一种带两个新损失函数的条件变分自编码器生成合成图像以补充数据。在五个基准图像数据集上进行大量实验，结果表明所提方法显著优于多个强基线。
原文摘要 · Abstract (English)
In image classification tasks, deep learning models are vulnerable to image distortion. For successful deployment, it is important to identify distortion levels under which the model is usable i.e. its accuracy stays above a stipulated threshold. We refer to this problem as Model Assurance under Image Distortion, and formulate it as a classification task. Given a distortion level, our goal is to predict if the model's accuracy on the set of distorted images is greater than a threshold. We propose a novel classifier based on a Level Set Estimation (LSE) algorithm, which uses the LSE's mean and variance functions to form the classification rule. We further extend our method to a "few sample" setting where we can only acquire few real images to perform the model assurance process. Our idea is to generate extra synthetic images using a novel Conditional Variational Autoencoder model with two new loss functions. We conduct extensive experiments to show that our classification method significantly outperforms strong baselines on five benchmark image datasets.
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