Presentation information

Organized Session

Organized Session » OS-10

[1J3-OS-10] OS-10

Tue. Jun 9, 2020 1:20 PM - 2:40 PM Room J (jsai2020online-10)

大西 正輝(産業技術総合研究所)、日野 英逸(統計数理研究所)、白川 真一(横浜国立大学)、秋本 洋平(筑波大学)

2:00 PM - 2:20 PM

[1J3-OS-10-03] Gradient-Based Architecture Search for Deep Multimodal Neural Networks

〇Yushiro Funoki1, Satoshi Ono1 (1. Kagoshima University)

Keywords:AI, Neural Architecture Search, Multimodal Learning

This paper proposes a gradient-based architecture search method for deep multimodal neural networks. Differentiable Architecture Search (DARTS), which is a gradient-based architecture search method, enables efficient architecture search of neural networks using a gradient descent method by defining the continuous search space. The proposed method is an extension of DARTS and specialized for deep multimodal neural networks. The proposed method can deal with variable-length sequential input data because it includes a Long Short-Term Memory (LSTM) as one of operators. Experiments with the emotion recognition dataset that includes time-series data have shown that the proposed method searched for the architecture that has competitive performance with the network manually designed in the previous work.

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