Papers › Online Deep Learning: Learning Deep Neural Networks on the Fly

Online Deep Learning: Learning Deep Neural Networks on the Fly

10 Nov 2017arXiv:1711.03705archive 2025-07-28

Doyen Sahoo, Quang Pham, Jing Lu, Steven C. H. Hoi

Deep Neural Networks (DNNs) are typically trained by backpropagation in a batch learning setting, which requires the entire training data to be made available prior to the learning task. This is not scalable for many real-world scenarios where new data arrives sequentially in a stream form. We aim to address an open challenge of "Online Deep Learning" (ODL) for learning DNNs on the fly in an online setting. Unlike traditional online learning that often optimizes some convex objective function with respect to a shallow model (e.g., a linear/kernel-based hypothesis), ODL is significantly more challenging since the optimization of the DNN objective function is non-convex, and regular backpropagation does not work well in practice, especially for online learning settings. In this paper, we present a new online deep learning framework that attempts to tackle the challenges by learning DNN models of adaptive depth from a sequence of training data in an online learning setting. In particular, we propose a novel Hedge Backpropagation (HBP) method for online updating the parameters of DNN effectively, and validate the efficacy of our method on large-scale data sets, including both stationary and concept drifting scenarios.

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LIBOL/ODL mentioned on GitHubpytorch report
Rohit102497/Aux-Drop mentioned on GitHubpytorch report
alison-carrera/onn mentioned on GitHubpytorchApache-2.0 report
meyresearch/online_vampnets mentioned on GitHubpytorchMIT report
phquang/OnlineDeepLearning mentioned on GitHub report

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Deep Learning

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Introduced by this paper: ODL

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