Abstract
This paper proposes a Neural Architecture Search (NAS) method for multimodal sequential data using a gradient-based neural architecture search method named Differentiable Neural Architecture Search (DARTS). Because Deep Neural Networks (DNNs) for multimodal data require task-specific network architecture, there is a high need for NAS for them to reduce the labor of architecture design. Experimental results using an emotion recognition dataset containing sequential data showed that the proposed method succeeded in automatically designing a network architecture with competitive performance to manually designed networks.
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