RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning

• URL: http://arxiv.org/abs/2003.01181v1
• Date: Mon, 2 Mar 2020 20:41:57 GMT
• Title: RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning
• Authors: Stefano Alletto, Shenyang Huang, Vincent Francois-Lavet, Yohei Nakata and Guillaume Rabusseau
• Abstract summary: We study the effectiveness of a random search strategy for fully automated multimodal neural architecture search. Compared to traditional methods that rely on manually crafted feature extractors, our method selects each modality from a large search space with minimal human supervision.
• Score: 7.5352209570833555
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Almost all neural architecture search methods are evaluated in terms of performance (i.e. test accuracy) of the model structures that it finds. Should it be the only metric for a good autoML approach? To examine aspects beyond performance, we propose a set of criteria aimed at evaluating the core of autoML problem: the amount of human intervention required to deploy these methods into real world scenarios. Based on our proposed evaluation checklist, we study the effectiveness of a random search strategy for fully automated multimodal neural architecture search. Compared to traditional methods that rely on manually crafted feature extractors, our method selects each modality from a large search space with minimal human supervision. We show that our proposed random search strategy performs close to the state of the art on the AV-MNIST dataset while meeting the desirable characteristics for a fully automated design process.

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