Dual Skipping Networks
Changmao Cheng, Yanwei Fu, Yu-Gang Jiang, Wei Liu, Wenlian Lu,, Jianfeng Feng, Xiangyang Xue

TL;DR
This paper introduces a dual skipping network inspired by neuroscience, enabling simultaneous coarse and fine object classification through a layer-skipping mechanism that predicts which layers to skip, demonstrating promising results on benchmark datasets.
Contribution
The paper proposes a novel dual skipping network with a learned gating mechanism for flexible layer skipping, enhancing coarse-to-fine object categorization.
Findings
Achieves promising results on benchmark datasets
Demonstrates effective coarse-to-fine classification
Introduces a flexible layer-skipping mechanism
Abstract
Inspired by the recent neuroscience studies on the left-right asymmetry of the human brain in processing low and high spatial frequency information, this paper introduces a dual skipping network which carries out coarse-to-fine object categorization. Such a network has two branches to simultaneously deal with both coarse and fine-grained classification tasks. Specifically, we propose a layer-skipping mechanism that learns a gating network to predict which layers to skip in the testing stage. This layer-skipping mechanism endows the network with good flexibility and capability in practice. Evaluations are conducted on several widely used coarse-to-fine object categorization benchmarks, and promising results are achieved by our proposed network model.
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Taxonomy
TopicsNeural Networks and Applications · EEG and Brain-Computer Interfaces · Blind Source Separation Techniques
