Transfer-learning and meta-learning are two effective methods to apply knowledge learned from large data sources to new tasks. In few-class, few-shot target task settings (i.e. when there are only a few classes and training examples available in the target task), meta-learning approaches that optimize for future task learning have outperformed the typical transfer approach of initializing model weights from a pre-trained starting point. But as we experimentally show, meta-learning algorithms that work well in the few-class setting do not generalize well in many-shot and many-class cases. In this paper, we propose a joint training approach that combines both transfer-learning and meta-learning. Benefiting from the advantages of each, our method obtains improved generalization performance on unseen target tasks in both few- and many-class and few- and many-shot scenarios.
09/21/2018 ∙ by Amir Erfan Eshratifar, et al. ∙ 0 ∙ share
JointDNN: An Efficient Training and Inference Engine for Intelligent Mobile Cloud Computing Services
Deep neural networks are among the most influential architectures of deep learning algorithms, being deployed in many mobile intelligent applications. End-side services, such as intelligent personal assistants (IPAs), autonomous cars, and smart home services often employ either simple local models or complex remote models on the cloud. Mobile-only and cloud-only computations are currently the status quo approaches. In this paper, we propose an efficient, adaptive, and practical engine, JointDNN, for collaborative computation between a mobile device and cloud for DNNs in both inference and training phase. JointDNN not only provides an energy and performance efficient method of querying DNNs for the mobile side, but also benefits the cloud server by reducing the amount of its workload and communications compared to the cloud-only approach. Given the DNN architecture, we investigate the efficiency of processing some layers on the mobile device and some layers on the cloud server. We provide optimization formulations at layer granularity for forward and backward propagation in DNNs, which can adapt to mobile battery limitations and cloud server load constraints and quality of service. JointDNN achieves up to 18X and 32X reductions on the latency and mobile energy consumption of querying DNNs, respectively.
01/25/2018 ∙ by Amir Erfan Eshratifar, et al. ∙ 0 ∙ share
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