Title: VCNet: Recreating High-Level Visual Cortex Principles for Robust Artificial Vision Show Chinese title

Title: VCNet：为稳健的人工视觉重现高级视觉皮层原理

Abstract: Despite their success in image classification, modern convolutional neural networks (CNNs) exhibit fundamental limitations, including data inefficiency, poor out-of-distribution generalization, and vulnerability to adversarial perturbations. The primate visual system, in contrast, demonstrates superior efficiency and robustness, suggesting that its architectural principles may offer a blueprint for more capable artificial vision systems. This paper introduces Visual Cortex Network (VCNet), a novel neural network architecture whose design is informed by the macro-scale organization of the primate visual cortex. VCNet emulates key biological mechanisms, including hierarchical processing across distinct cortical areas, dual-stream information segregation, and top-down predictive feedback. We evaluate VCNet on two specialized benchmarks: the Spots-10 animal pattern dataset and a light field image classification task. Our results show that VCNet achieves a classification accuracy of 92.1\% on Spots-10 and 74.4\% on the light field dataset, surpassing contemporary models of comparable size. This work demonstrates that integrating neuroscientific principles into network design can lead to more efficient and robust models, providing a promising direction for addressing long-standing challenges in machine learning. Show Chinese abstract

Abstract: 尽管在图像分类中取得了成功，现代卷积神经网络（CNNs）表现出基本的局限性，包括数据效率低下、分布外泛化能力差以及对对抗扰动的脆弱性。 相比之下，灵长类视觉系统表现出更高的效率和鲁棒性，这表明其架构原理可能为更强大的人工视觉系统提供蓝图。 本文介绍了视觉皮层网络（VCNet），这是一种新型神经网络架构，其设计受到灵长类视觉皮层宏观组织的启发。 VCNet模拟了关键的生物机制，包括不同皮层区域之间的分层处理、双流信息分离以及自上而下的预测反馈。 我们在两个专业基准上评估了VCNet：Spots-10动物图案数据集和光场图像分类任务。 我们的结果表明，VCNet在Spots-10上的分类准确率为92.1%，在光场数据集上的分类准确率为74.4%，超过了同等规模的当代模型。 这项工作表明，将神经科学原理整合到网络设计中可以导致更高效和更鲁棒的模型，为解决机器学习中的长期挑战提供了有前景的方向。