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• W3034042817 abstract "Designing resource-efficient Deep Neural Networks (DNNs) is critical to deploy deep learning solutions over edge platforms due to diverse performance, power, and memory budgets. Unfortunately, it is often the case a well-trained ML model does not fit to the constraint of deploying edge platforms, causing a long iteration of model reduction and retraining process. Moreover, a ML model optimized for platform-A often may not be suitable when we deploy it on another platform-B, causing another iteration of model retraining. We propose a conditional neural architecture search method using GAN, which produces feasible ML models for different platforms. We present a new workflow to generate constraint-optimized DNN models. This is the first work of bringing in condition and adversarial technique into Neural Architecture Search domain. We verify the method with regression problems and classification on CIFAR-10. The proposed workflow can successfully generate resource-optimized MLP or CNN-based networks." @default.
• W3034042817 created "2020-06-12" @default.
• W3034042817 creator A5021206737 @default.
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• W3034042817 date "2020-06-06" @default.
• W3034042817 modified "2023-09-27" @default.
• W3034042817 title "Conditional Neural Architecture Search." @default.
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• W3034042817 hasPublicationYear "2020" @default.
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