层级复杂度匹配学习产生改进的皮层V2区模型
Layerwise complexity-matched learning yields an improved model of cortical area V2
AI总结：
提出层级复杂度匹配学习（LCL），通过自下而上自监督训练匹配各层任务复杂度，生成改进的V2区模型，并提升分布外泛化与人类行为一致性。
摘要：
人类识别复杂视觉模式的能力源于腹侧视觉皮层中连续区域所进行的变换。端到端训练用于物体识别的深度神经网络接近人类能力，并提供了迄今为止对层级后期阶段神经反应的最佳描述。但与传统手工设计的模型或针对编码效率或预测优化的模型相比，这些网络对早期阶段的解释较差。此外，端到端学习中使用的梯度反向传播通常被认为在生物学上不可信。在此，我们通过开发一种自下而上的自监督训练方法克服了这两个限制，该方法独立作用于连续层。具体而言，我们最大化局部变形自然图像补丁对之间的特征相似性，同时去相关来自其他图像采样的补丁之间的特征。关键在于，变形幅度根据每层的感受野大小按比例调整，从而使任务复杂度与每个处理阶段的能力相匹配。与先前模型的架构匹配版本相比，我们证明我们的层级复杂度匹配学习（LCL）公式产生了一个两阶段模型（LCL-V2），该模型与灵长类动物V2区的选择性特性和神经活动更好地对齐。我们证明复杂度匹配学习范式是改善生物对齐的大部分原因。最后，当两阶段模型用作训练执行物体识别的深度网络的固定前端时，所得模型（LCL-V2Net）在分布外任务的泛化能力和与人类行为的一致性方面显著优于标准端到端自监督、监督和对抗训练模型。
英文摘要：
Human ability to recognize complex visual patterns arises through transformations performed by successive areas in the ventral visual cortex. Deep neural networks trained end-to-end for object recognition approach human capabilities, and offer the best descriptions to date of neural responses in the late stages of the hierarchy. But these networks provide a poor account of the early stages, compared to traditional hand-engineered models, or models optimized for coding efficiency or prediction. Moreover, the gradient backpropagation used in end-to-end learning is generally considered to be biologically implausible. Here, we overcome both of these limitations by developing a bottom-up self-supervised training methodology that operates independently on successive layers. Specifically, we maximize feature similarity between pairs of locally-deformed natural image patches, while decorrelating features across patches sampled from other images. Crucially, the deformation amplitudes are adjusted proportionally to receptive field sizes in each layer, thus matching the task complexity to the capacity at each stage of processing. In comparison with architecture-matched versions of previous models, we demonstrate that our layerwise complexity-matched learning (LCL) formulation produces a two-stage model (LCL-V2) that is better aligned with selectivity properties and neural activity in primate area V2. We demonstrate that the complexity-matched learning paradigm is responsible for much of the emergence of the improved biological alignment. Finally, when the two-stage model is used as a fixed front-end for a deep network trained to perform object recognition, the resultant model (LCL-V2Net) is significantly better than standard end-to-end self-supervised, supervised, and adversarially-trained models in terms of generalization to out-of-distribution tasks and alignment with human behavior.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
