Deep Multimodal Transfer-Learned Regression in Data-Poor Domains

• URL: http://arxiv.org/abs/2006.09310v1
• Date: Tue, 16 Jun 2020 16:52:44 GMT
• Title: Deep Multimodal Transfer-Learned Regression in Data-Poor Domains
• Authors: Levi McClenny, Mulugeta Haile, Vahid Attari, Brian Sadler, Ulisses Braga-Neto, Raymundo Arroyave
• Abstract summary: We propose a Deep Multimodal Transfer-Learned Regressor (DMTL-R) for multimodal learning of image and feature data. Our model is capable of fine-tuning a given set of pre-trained CNN weights on a small amount of training image data. We present results using phase-field simulation microstructure images with an accompanying set of physical features, using pre-trained weights from various well-known CNN architectures.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In many real-world applications of deep learning, estimation of a target may rely on various types of input data modes, such as audio-video, image-text, etc. This task can be further complicated by a lack of sufficient data. Here we propose a Deep Multimodal Transfer-Learned Regressor (DMTL-R) for multimodal learning of image and feature data in a deep regression architecture effective at predicting target parameters in data-poor domains. Our model is capable of fine-tuning a given set of pre-trained CNN weights on a small amount of training image data, while simultaneously conditioning on feature information from a complimentary data mode during network training, yielding more accurate single-target or multi-target regression than can be achieved using the images or the features alone. We present results using phase-field simulation microstructure images with an accompanying set of physical features, using pre-trained weights from various well-known CNN architectures, which demonstrate the efficacy of the proposed multimodal approach.

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