Web: http://arxiv.org/abs/2209.08310

Sept. 20, 2022, 1:12 a.m. | Yizeng Han, Yifan Pu, Zihang Lai, Chaofei Wang, Shiji Song, Junfen Cao, Wenhui Huang, Chao Deng, Gao Huang

cs.CV updates on arXiv.org arxiv.org

Early exiting is an effective paradigm for improving the inference efficiency
of deep networks. By constructing classifiers with varying resource demands
(the exits), such networks allow easy samples to be output at early exits,
removing the need for executing deeper layers. While existing works mainly
focus on the architectural design of multi-exit networks, the training
strategies for such models are largely left unexplored. The current
state-of-the-art models treat all samples the same during training. However,
the early-exiting behavior during testing …

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