دسته بندی:

بینایی ماشین - Machine vision

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سال انتشار:

2022

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عنوان انگلیسی مقاله:

VisuaLizations As Intermediate Representations (VLAIR): An approach for applying deep learning-based computer vision to non-image-based data

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ترجمه فارسی عنوان مقاله:

تجسم ها به عنوان بازنمایی های میانی (VLAIR): رویکردی برای به کارگیری بینایی کامپیوتری مبتنی بر یادگیری عمیق برای داده های غیر مبتنی بر تصویر

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منبع:

ScienceDirect- Elsevier- Visual Informatics, 6 (2022) 35-50: doi:10:1016/j:visinf:2022:05:001

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نویسنده:

None

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چکیده انگلیسی:

Deep learning algorithms increasingly support automated systems in areas such as human activity recognition and purchase recommendation. We identify a current trend in which data is transformed first into abstract visualizations and then processed by a computer vision deep learning pipeline. We call this VisuaLization As Intermediate Representation (VLAIR) and believe that it can be instrumental to support accurate recognition in a number of fields while also enhancing humans’ ability to interpret deep learning models for debugging purposes or for personal use. In this paper we describe the potential advantages of this approach and explore various visualization mappings and deep learning architectures. We evaluate several VLAIR alternatives for a specific problem (human activity recognition in an apartment) and show that VLAIR attains classification accuracy above classical machine learning algorithms and several other non-image-based deep learning algorithms with several data representations.
keywords: تجسم اطلاعات | شبکه های عصبی کانولوشنال | تشخیص فعالیت های انسانی | خانه های هوشمند | بازنمایی داده ها | نمایندگی های میانی | تفسیر پذیری | یادگیری ماشین | یادگیری عمیق | Information visualization | Convolutional neural networks | Human activity recognition | Smart homes | Data representation | Intermediate representations | Interpretability | Machine learning | Deep learning

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قیمت: رایگان

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توضیحات اضافی:

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