Deep Neural Networks (DNNs) that achieve human-level performance in general
tasks like object segmentation typically require supervised labels. In
contrast, humans are able to perform these tasks effortlessly without
supervision. To accomplish this, the human visual system makes use of
perceptual grouping. Understanding how perceptual grouping arises in an
unsupervised manner is critical for improving both models of the visual system,
and computer vision models. In this work, we propose a counterintuitive
approach to unsupervised perceptual grouping and segmentation: that they arise
because of neural noise, rather than in spite of it. We (1) mathematically
demonstrate that under realistic assumptions, neural noise can be used to
separate objects from each other, and (2) show that adding noise in a DNN
enables the network to segment images even though it was never trained on any
segmentation labels. Interestingly, we find that (3) segmenting objects using
noise results in segmentation performance that aligns with the perceptual
grouping phenomena observed in humans. We introduce the Good Gestalt (GG)
datasets -- six datasets designed to specifically test perceptual grouping, and
show that our DNN models reproduce many important phenomena in human
perception, such as illusory contours, closure, continuity, proximity, and
occlusion. Finally, we (4) demonstrate the ecological plausibility of the
method by analyzing the sensitivity of the DNN to different magnitudes of
noise. We find that some model variants consistently succeed with remarkably
low levels of neural noise ($\sigma<0.001$), and surprisingly, that segmenting
this way requires as few as a handful of samples. Together, our results suggest
a novel unsupervised segmentation method requiring few assumptions, a new
explanation for the formation of perceptual grouping, and a potential benefit
of neural noise in the visual system.

通过数学演示，在现实假设下，神经噪声可以用于分离对象，并且在 DNN 网络中添加噪声使其能够对图像进行分割，从而产生与人类感知中观察到的感知分组现象一致的分段性能。