English(EN) Sparse Competition during Training For the Emergence of Specialized Modules

新训练方法促进神经网络中专业化模块的出现

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员开发了一种新的训练方法，该方法能促进深度神经网络中专业化模块的出现。该方法在保持基线准确率的同时，将输入稀疏地路由到神经元组，从而促进专业化，使模块能够响应特定的输入类别，如“狗”或“车辆”。该研究在 ImageNet-100 和 CIFAR-100 数据集上进行了评估，表明竞争动态可以在标准的神经网络架构中自然地诱导功能模块化，甚至根据模块数量揭示分层的任务划分。 AI

影响这项研究通过促进专业化模块，有望实现更具可解释性和效率的神经网络训练。

排序理由这是一篇详细介绍神经网络新训练方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Baptiste Rossigneux, Karim Haroun · 2026-09-01 04:00

训练中稀疏竞争促使专业化模块涌现

arXiv:2608.30978v1 Announce Type: new Abstract: Modularity in deep neural networks has been proposed as a means of improving both interpretability and training by promoting disentangled representations and reducing redundancy. In this work, we study the emergence of modular struc…

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训练中稀疏竞争促使专业化模块涌现

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