Pool of Experts: Realtime Querying Specialized Knowledge in Massive Neural Networks
This addresses the need for efficient model deployment on mobile and embedded systems, offering a novel approach to reduce latency and resource usage.
The paper tackles the problem of time-consuming training and model compression for deploying deep learning models on resource-limited devices by proposing Pool of Experts (PoE), a framework that builds lightweight, task-specific models in realtime without training, achieving similar accuracy to methods that take minutes per query.
In spite of the great success of deep learning technologies, training and delivery of a practically serviceable model is still a highly time-consuming process. Furthermore, a resulting model is usually too generic and heavyweight, and hence essentially goes through another expensive model compression phase to fit in a resource-limited device like embedded systems. Inspired by the fact that a machine learning task specifically requested by mobile users is often much simpler than it is supported by a massive generic model, this paper proposes a framework, called Pool of Experts (PoE), that instantly builds a lightweight and task-specific model without any training process. For a realtime model querying service, PoE first extracts a pool of primitive components, called experts, from a well-trained and sufficiently generic network by exploiting a novel conditional knowledge distillation method, and then performs our train-free knowledge consolidation to quickly combine necessary experts into a lightweight network for a target task. Thanks to this train-free property, in our thorough empirical study, PoE can build a fairly accurate yet compact model in a realtime manner, whereas it takes a few minutes per query for the other training methods to achieve a similar level of the accuracy.