ResNets implicitly route by varying residual interaction experts

A new research paper explores the internal workings of Residual Networks (ResNets), suggesting they implicitly perform a form of soft routing. By analyzing ResNet-18 and ResNet-34 models trained on ImageNet, the study found that while all network blocks execute for every input, the specific interactions between these blocks vary based on the input and predicted class. This suggests that different inputs leverage distinct "expert" interactions within the network, even without an explicit routing mechanism. AI

IMPACT Suggests a new understanding of how deep learning models process information, potentially influencing future architectural designs.

RANK_REASON Academic paper detailing novel findings about model architecture. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• ImageNet
• ResNet
• ResNet-18
• ResNet-34

• paper
• model release

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