Online Deep Learning: Learning Deep Neural Networks on the Fly (1711.03705v1)

Abstract: 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.

• The paper presents a novel framework that employs Hedge Backpropagation to dynamically adjust DNN depth for online learning.
• The adaptive method balances rapid shallow-layer convergence with deeper, long-term predictive power, significantly reducing cumulative error rates.
• Extensive experiments demonstrate the approach’s effectiveness in streaming data scenarios, offering promising applications in real-time analytics and IoT.

Online Deep Learning: Learning Deep Neural Networks on the Fly

The paper "Online Deep Learning: Learning Deep Neural Networks on the Fly" by Doyen Sahoo, Quang Pham, Steven C.H. Hoi, and Jing Lu addresses the formidable challenge of training deep neural networks (DNNs) in an online setting, where data arrives sequentially in a stream form. Traditional deep learning approaches that rely on batch training demand access to the entire dataset at once, which is infeasible in real-world applications characterized by continuous data inflow and potential memory constraints. The authors propose a novel framework, termed Online Deep Learning (ODL), aimed at surmounting these challenges.

Motivation and Problem Statement

The motivation behind this paper is the existing limitation in applying traditional deep learning methods to online learning scenarios. Conventional approaches optimize shallow models using online convex optimization techniques, which are incompatible with the complex nonlinear functions characterizing deep networks. Online Deep Learning aims to fill this gap by devising mechanisms that allow for the adaptation of a DNN’s depth in response to evolving data complexity, initiating training with a shallower network structure for rapid convergence and progressively deepening as more data becomes available.

Hedge Backpropagation (HBP)

At the core of the proposed framework is Hedge Backpropagation (HBP), an innovative method that dynamically adjusts the learning capacity of a DNN. HBP introduces the concept of adaptive depth, making use of a Hedge algorithm to balance predictions from models of varying depths. This method enables the development of a deep neural network architecture where each hidden layer is coupled with an independent output classifier. By employing the Hedge algorithm, the model adapts the weights of these classifiers based on their performance, facilitating a seamless transition from shallow to deeper representations as required by the data.

This strategic adaptation not only improves convergence by capitalizing on the swift learning of shallower models but also leverages deeper networks for enhanced long-term predictive power. The technique addresses traditional constraints posed by vanishing gradients and diminishing feature reuse, which are commonplace in deep learning optimization, particularly in an online context.

Experimental Validation and Results

Through extensive experimentation across several datasets, including synthetic and real-world datasets that simulate both stationary and concept-drifting environments, the authors demonstrate the efficacy of the HBP method. Notably, HBP outperforms standard online backpropagation and various gradient descent enhancements (e.g., Nesterov and Momentum techniques) across different stages of learning and various network depths, affirming its robustness and adaptability. By mitigating depth-related model selection dilemmas and promoting effective knowledge sharing across layers, HBP achieves significant reductions in online cumulative error rates compared to established baselines.

Implications and Future Directions

The implications of this research are considerable for real-time analytics and applications requiring continuous learning from data streams. By equipping DNNs with the capability to adaptively scale their learning complexity, ODL stands to enhance the utility of neural networks in evolving data landscapes, including IoT and big data analytics.

Looking forward, there exists notable potential for extending this framework to other neural architectures, such as convolutional networks, in settings where rapid inference and adaptation are crucial. Future work might also explore the integration of HBP in reinforcement learning environments, where dynamic learning capacities could yield improved decision-making processes. The scalability and optimization of such adaptive frameworks in distributed computing settings present additional promising avenues for research.

In summary, this paper presents a compelling approach that aligns deep learning capabilities with the exigencies of online learning, marking a significant stride toward deploying scalable and efficient DNNs in dynamic, real-world environments.