Some recent artificial neural networks (ANNs) claim to model aspects of primate neural and human performance data. Their success in object recognition is, however, dependent on exploiting low-level features for solving visual tasks in a way that humans do not. As a result, out-of-distribution or adversarial input is often challenging for ANNs. Humans instead learn abstract patterns and are mostly unaffected by many extreme image distortions. We introduce a set of novel image transforms inspired by neurophysiological findings and evaluate humans and ANNs on an object recognition task. We show that machines perform better than humans for certain transforms and struggle to perform at par with humans on others that are easy for humans. We quantify the differences in accuracy for humans and machines and find a ranking of difficulty for our transforms for human data. We also suggest how certain characteristics of human visual processing can be adapted to improve the performance of ANNs for our difficult-for-machines transforms.

翻译：近期部分人工神经网络声称能模拟灵长类动物神经及人类行为数据特性。然而，这些网络在目标识别任务上的成功往往依赖于对人类不擅长的低层级特征提取。由此产生的分布外或对抗性输入常使人工神经网络陷入困境。相比之下，人类通过学习抽象模式来规避多数极端图像畸变带来的干扰。本研究借鉴神经生理学发现，引入一组新型图像变换方法，并在目标识别任务中评估人机表现差异。实验证明，机器在特定变换条件下优于人类，但在人类易于处理的其他变换任务中难以与人类比肩。我们量化了人机准确率差异，并建立了人类数据在各类变换中的难度排序。此外，我们提出可将人类视觉处理机制的某些特性迁移至人工神经网络中，以提升其在机器困难型变换任务中的性能表现。