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- W4225129812 abstract "Multi-exit architectures consist of a backbone and branch classifiers that offer shortened inference pathways to reduce the run-time of deep neural networks. In this paper, we analyze different branching patterns that vary in their allocation of computational complexity for the branch classifiers. Constant-complexity branching keeps all branches the same, while complexity-increasing and complexity-decreasing branching place more complex branches later or earlier in the backbone respectively. Through extensive experimentation on multiple backbones and datasets, we find that complexity-decreasing branches are more effective than constant-complexity or complexity-increasing branches, which achieve the best accuracy-cost trade-off. We investigate a cause by using knowledge consistency to probe the effect of adding branches onto a backbone. Our findings show that complexity-decreasing branching yields the least disruption to the feature abstraction hierarchy of the backbone, which explains the effectiveness of the branching patterns." @default.
- W4225129812 created "2022-05-01" @default.
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- W4225129812 date "2022-04-28" @default.
- W4225129812 modified "2023-09-27" @default.
- W4225129812 title "A Closer Look at Branch Classifiers of Multi-exit Architectures" @default.
- W4225129812 doi "https://doi.org/10.48550/arxiv.2204.13347" @default.
- W4225129812 hasPublicationYear "2022" @default.
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