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Network pruning impacts GoogLeNet performance and interpretability

By PulseAugur Editorial · [1 sources] ·

Researchers investigated how network pruning affects the performance and interpretability of GoogLeNet on ImageNet. They applied various pruning techniques and retraining strategies, finding that performance could be maintained or even improved with sufficient retraining. However, their experiments using the Mechanistic Interpretability Score (MIS) showed no clear link between pruning rate and interpretability, suggesting MIS may not always align with intuitive understanding of model decisions. AI

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RANK_REASON This is a research paper detailing experiments on neural network pruning techniques and their effects on performance and interpretability. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Network pruning impacts GoogLeNet performance and interpretability

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This is a research paper detailing experiments on neural network pruning techniques and their effects on performance and interpretability. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jonathan von Rad, Florian Seuffert ·

    Investigating the Effect of Network Pruning on Performance and Interpretability

    arXiv:2409.19727v3 Announce Type: replace Abstract: Deep Neural Networks (DNNs) are often over-parameterized for their tasks and can be compressed quite drastically by removing weights, a process called pruning. We investigate the impact of different pruning techniques on the cla…

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