论文标题
人类和CNN中强大的翻译耐受性的案例。对Han等人的评论
A case for robust translation tolerance in humans and CNNs. A commentary on Han et al
论文作者
论文摘要
Han等。 （2020）报道了一个行为实验，该实验评估了人类视觉系统可以在看不见的视网膜位置（作者称之为“固有翻译不变性”）的多大程度，并开发了一种新型的卷积神经网络模型（偏心率依赖性网络或ENN），以捕获行为结果的关键方面。在这里，我们表明他们对行为数据的分析使用了不适当的基线条件，从而导致他们低估了内在的翻译不变性。当正确解释数据时，它们在某些条件下显示出几乎完全的翻译公差延伸至14°，这与早期的工作一致（Bowers等，2016）和更近期的工作Blything等。 （在印刷中）。我们描述了一个更简单的模型，可以更好地说明翻译不变性。
Han et al. (2020) reported a behavioral experiment that assessed the extent to which the human visual system can identify novel images at unseen retinal locations (what the authors call "intrinsic translation invariance") and developed a novel convolutional neural network model (an Eccentricity Dependent Network or ENN) to capture key aspects of the behavioral results. Here we show that their analysis of behavioral data used inappropriate baseline conditions, leading them to underestimate intrinsic translation invariance. When the data are correctly interpreted they show near complete translation tolerance extending to 14° in some conditions, consistent with earlier work (Bowers et al., 2016) and more recent work Blything et al. (in press). We describe a simpler model that provides a better account of translation invariance.
