Machine learning models have shown increased accuracy in classification tasks when the training process incorporates human perceptual information. However, a challenge in training human-guided models is the cost associated with collecting image annotations for human salience. Collecting annotation data for all images in a large training set can be prohibitively expensive. In this work, we utilize ''teacher'' models (trained on a small amount of human-annotated data) to annotate additional data by means of teacher models' saliency maps. Then, ''student'' models are trained using the larger amount of annotated training data. This approach makes it possible to supplement a limited number of human-supplied annotations with an arbitrarily large number of model-generated image annotations. We compare the accuracy achieved by our teacher-student training paradigm with (1) training using all available human salience annotations, and (2) using all available training data without human salience annotations. We use synthetic face detection and fake iris detection as example challenging problems, and report results across four model architectures (DenseNet, ResNet, Xception, and Inception), and two saliency estimation methods (CAM and RISE). Results show that our teacher-student training paradigm results in models that significantly exceed the performance of both baselines, demonstrating that our approach can usefully leverage a small amount of human annotations to generate salience maps for an arbitrary amount of additional training data.

翻译：机器学习模型在训练过程中融入人类感知信息时，分类任务的准确率已显著提升。然而，训练人类引导模型面临的挑战之一是收集图像的人类显著性标注成本高昂。为大规模训练集中的所有图像采集标注数据可能代价过高。本研究利用"教师"模型（基于少量人工标注数据训练）通过其显著性图为额外数据生成标注，随后"学生"模型使用更大规模的标注训练数据进行训练。该方法使得有限的人工标注可被任意规模模型生成的图像标注进行补充。我们将师生训练范式实现的准确率与以下两种方案进行对比：（1）使用全部可用的人类显著性标注进行训练；（2）使用全部可用训练数据（不含人类显著性标注）。本研究以合成人脸检测和伪造虹膜检测为例，在四种模型架构（DenseNet、ResNet、Xception和Inception）及两种显著性估计方法（CAM和RISE）上报告结果。实验表明，我们的师生训练范式产生的模型性能显著优于两种基线方案，证明该方法能有效利用少量人工标注为任意规模的额外训练数据生成显著性图。