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arXiv 2605.13675cs.CVcs.LGq-bio.NC
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跨视觉模型的通用对象表示表征

Characterizing universal object representations across vision models

Vision and Computational Cognition Group(视觉与计算认知组);Max Planck Institute(马克斯·普朗克研究所);Justus-Liebig-University Giessen(吉森约瑟夫·李贝大学);Machine Learning Core(机器学习核心);Department of Cognitive Science(认知科学系);National Institute of Mental Health(国家心理健康研究所);Johns Hopkins University(约翰霍普金斯大学)

Florian P. Mahner, Johannes Roth, Ka Chun Lam, Michael F. Bonner, Francisco Pereira, Martin N. Hebart

AI总结:

研究通过分解162种不同视觉模型的对象相似性结构,发现通用维度比模型特定维度更可解释,且与概念图像属性相关,表明生物视觉与深度学习模型的对齐。

摘要:

深度神经网络在不同架构、目标和数据集上训练后,已被报告收敛于相似的视觉表示。然而,尚不清楚模型实际收敛于哪些视觉属性以及导致这种收敛的因素。为此,我们将162种多样化视觉模型的对象相似性结构分解为少量非负维度。通过估计每个维度在不同模型中重新出现的频率,确定通用维度与模型特定维度的区别。与模型特定维度不同,通用维度更可解释且更强烈由概念图像属性驱动,表明可解释性和语义内容作为隐含因素驱动跨模型的通用性。架构、目标函数、训练数据、模型大小和性能差异无法解释通用维度的出现。然而,具有更多通用维度的模型在预测恒河猴IT活动和人类相似性判断方面表现更好,表明通用性反映了与生物视觉相关的表示。这些发现对理解深度神经网络模型的涌现表示及其与生物视觉的一致性具有重要影响。

英文摘要:

Deep neural networks trained with different architectures, objectives, and datasets have been reported to converge on similar visual representations. However, what remains unknown is which visual properties models actually converge on and which factors may underlie this convergence. To address this, we decompose the object similarity structure of 162 diverse vision models into a small set of non-negative dimensions. To determine universal versus model-specific dimensions, we then estimate how often each dimension reappears across models. In contrast to model-specific dimensions, universal dimensions are more interpretable and more strongly driven by conceptual image properties, indicating the relevance of interpretability and semantic content as implicit factors driving universality across models. Differences in architecture, objective function, training data, model size, and model performance do not explain the emergence of universal dimensions. However, models with more universal dimensions also better predict macaque IT activity and human similarity judgments, suggesting that universality reflects representations relevant to biological vision. These findings have important implications for understanding the emergent representations underlying deep neural network models and their alignment with biological vision.

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。

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