标题： 具有神经启发前端的显式建模子皮层视觉可提高CNN鲁棒性 显示英文标题

标题： Explicitly Modeling Subcortical Vision with a Neuro-Inspired Front-End Improves CNN Robustness

摘要： 基于物体识别训练的卷积神经网络（CNN）在任务表现上很高，但与生物视觉相比，在各种视觉扰动和域外图像下仍然表现出脆弱性。先前的研究表明，将标准CNN与前端块（VOneBlock）耦合，该块模仿灵长类初级视觉皮层（V1），可以提高整体模型的鲁棒性。 在此基础上，我们引入了早期视觉网络（EVNets），这是一种新的混合CNN类别，它结合了VOneBlock和一种新颖的SubcorticalBlock，其架构借鉴了神经科学中的计算模型，并且参数化以最大化与多个实验研究中报告的亚皮质响应的一致性。 无需优化即可实现SubcorticalBlock与VOneBlock的组合，在大多数标准V1基准测试中提高了V1一致性，并更好地模拟了经典感受野之外的现象。 此外，EVNets表现出更强的形状偏差，并在包括对抗性扰动、常见腐败和领域转移在内的鲁棒性评估综合基准测试中比基础CNN架构高出8.5%。 最后，我们显示当与最先进的数据增强技术配对时，EVNets可以进一步改进，从而在我们的鲁棒性基准测试中比单独的数据增强方法高出7.3%的性能。 这一结果揭示了在模仿生物方面改变架构和基于训练的机器学习方法之间的互补优势。 显示英文摘要

摘要： Convolutional neural networks (CNNs) trained on object recognition achieve high task performance but continue to exhibit vulnerability under a range of visual perturbations and out-of-domain images, when compared with biological vision. Prior work has demonstrated that coupling a standard CNN with a front-end block (VOneBlock) that mimics the primate primary visual cortex (V1) can improve overall model robustness. Expanding on this, we introduce Early Vision Networks (EVNets), a new class of hybrid CNNs that combine the VOneBlock with a novel SubcorticalBlock, whose architecture draws from computational models in neuroscience and is parameterized to maximize alignment with subcortical responses reported across multiple experimental studies. Without being optimized to do so, the assembly of the SubcorticalBlock with the VOneBlock improved V1 alignment across most standard V1 benchmarks, and better modeled extra-classical receptive field phenomena. In addition, EVNets exhibit stronger emergent shape bias and overperform the base CNN architecture by 8.5% on an aggregate benchmark of robustness evaluations, including adversarial perturbations, common corruptions, and domain shifts. Finally, we show that EVNets can be further improved when paired with a state-of-the-art data augmentation technique, surpassing the performance of the isolated data augmentation approach by 7.3% on our robustness benchmark. This result reveals complementary benefits between changes in architecture to better mimic biology and training-based machine learning approaches.