Decision letter: Emergence of brain-like mirror-symmetric viewpoint tuning in convolutional neural networks
2023
Primates can recognize objects despite 3D geometric variations such as in-depth rotations.The computational mechanisms that give rise to such invariances are yet to be fully understood.A curious case of partial invariance occurs in the macaque face-patch AL and in fully connected layers of deep convolutional networks in which neurons respond similarly to mirrorsymmetric views (e.g.left and right profiles).Why does this tuning develop?Here, we propose a simple learning-driven explanation for mirror-symmetric viewpoint tuning.We show that mirrorsymmetric viewpoint tuning for faces emerges in the fully connected layers of convolutional deep neural networks trained on object recognition tasks, even when the training dataset does not include faces.First, using 3D objects rendered from multiple views as test stimuli, we demonstrate that mirror-symmetric viewpoint tuning in convolutional neural network models is not unique to faces: it emerges for multiple object categories with bilateral symmetry.Second, we show why this invariance emerges in the models.Learning to discriminate among bilaterally symmetric object categories induces reflection-equivariant intermediate representations.AL-like mirror-symmetric tuning is achieved when such equivariant responses are spatially pooled by downstream units with sufficiently large receptive fields.These results explain how mirror-symmetric viewpoint tuning can emerge in neural networks, providing a theory of how they might emerge in the primate brain.Our theory predicts that mirror-symmetric viewpoint tuning can emerge as a consequence of exposure to bilaterally symmetric objects beyond the category of faces, and that it can generalize beyond previously experienced object categories. Editor's evaluationThis computational study is a valuable empirical investigation into the common trait of neurons in brains and artificial neural networks: responding effectively to both objects and their mirror images and it focuses on uncovering conditions that lead to mirror symmetry in visual networks and the evidence convincingly demonstrates that learning contributes to expanding mirror symmetry tuning, given its presence in the data.Additionally, the paper delves into the transformation of face patches in primate visual hierarchy, shifting from view specificity to mirror symmetry to view invariance.It empirically analyzes factors behind similar effects in many network architectures, and key claims