Abstract
We propose a guided dropout regularizer for deep networks based on the evidence of a network prediction defined as the firing of neurons in specific paths. In this work, we utilize the evidence at each neuron to determine the probability of dropout, rather than dropping out neurons uniformly at random as in standard dropout. In essence, we dropout with higher probability those neurons which contribute more to decision making at training time. This approach penalizes high saliency neurons that are most relevant for model prediction, i.e. those having stronger evidence. By dropping such high-saliency neurons, the network is forced to learn alternative paths in order to maintain loss minimization, resulting in a plasticity-like behavior, a characteristic of human brains too. We demonstrate better generalization ability, an increased utilization of network neurons, and a higher resilience to network compression using several metrics over four image/video recognition benchmarks.
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Acknowledgements
This work was supported in part by the Defense Advanced Research Projects Agency (DARPA) Explainable Artificial Intelligence (XAI) program, an IBM PhD Fellowship, a Hariri Graduate Fellowship, and gifts from Adobe and NVidia. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of DARPA, DOI/IBC, or the U.S. Government.
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Communicated by Nikos KOMODAKIS.
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This work was done when A. Zunino was in PAVIS, at Istituto Italiano di Tecnologia.
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Zunino, A., Bargal, S.A., Morerio, P. et al. Excitation Dropout: Encouraging Plasticity in Deep Neural Networks. Int J Comput Vis 129, 1139–1152 (2021). https://doi.org/10.1007/s11263-020-01422-y
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DOI: https://doi.org/10.1007/s11263-020-01422-y