Abstract
Deep learning-based models generalize better to unknown data samples after being guided "where to look" by incorporating human perception into training strategies. We made an observation that the entropy of the model's salience trained in that way is lower when compared to salience entropy computed for models training without human perceptual intelligence. Thus the question: does further increase of model's focus, by lowering the entropy of model's class activation map, help in further increasing the performance? In this paper we propose and evaluate several entropy-based new loss function components controlling the model's focus, covering the full range of the level of such control, from none to its "aggressive" minimization. We show, using a problem of synthetic face detection, that improving the model's focus, through lowering entropy, leads to models that perform better in an open-set scenario, in which the test samples are synthesized by unknown generative models. We also show that optimal performance is obtained when the model's loss function blends three aspects: regular classification, low-entropy of the model's focus, and human-guided saliency.
Abstract (translated)
在通过将人类感知纳入训练策略来引导“看哪里”时,使用深度学习模型对未知数据样本的泛化能力更好。我们观察到,与没有人类感知 intelligence 训练的模型计算的感知熵相比,这种训练方法训练的模型熵更低。因此,问题变成了:进一步增加模型的关注程度,通过降低模型的类激活图熵,是否能够进一步增加性能?在本文中,我们提出了和评估了几个基于熵的新损失函数组件,用于控制模型的关注,涵盖了从没有控制到“激进”最小化的所有水平。我们使用合成人脸检测问题来证明,通过降低熵,改善模型的关注会导致在开放场景下测试样本表现更好的模型。我们还证明,当模型的损失函数融合了三个方面:常规分类、模型关注熵的较低熵以及人类指导的感知熵时,能够获得最佳性能。
URL
https://arxiv.org/abs/2303.00818
