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Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks

arXiv 1512.05830 ↗First posted 18 Dec 2015cs.CV cs.LGcited by 115

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Abstract (arXiv)

Learning deeper convolutional neural networks becomes a tendency in recent years. However, many empirical evidences suggest that performance improvement cannot be gained by simply stacking more layers. In this paper, we consider the issue from an information theoretical perspective, and propose a novel method Relay Backpropagation, that encourages the propagation of effective information through the network in training stage. By virtue of the method, we achieved the first place in ILSVRC 2015 Scene Classification Challenge. Extensive experiments on two challenging large scale datasets demonstrate the effectiveness of our method is not restricted to a specific dataset or network architecture. Our models will be available to the research community later.

Key facts. Cited by 115 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Dec 2015.

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Citing papers per year: 2021: 25; 2022: 30; 2023: 27; 2024: 21; 2025: 9; 2026: 3.

115 papers.

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