The growth of machine learning (ML) in environmental science can be divided into a slow phase lasting till the mid-2010s and a fast phase thereafter. The rapid transition was brought about by the emergence of powerful...
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The growth of machine learning (ML) in environmental science can be divided into a slow phase lasting till the mid-2010s and a fast phase thereafter. The rapid transition was brought about by the emergence of powerful new ML methods, allowing ML to successfully tackle many problems where numerical models and statistical models have been hampered. Deep convolutional neural network models greatly advanced the use of ML on 2D or 3D data. Transfer learning has allowed ML to progress in climate science, where data records are generally short for ML. ML and physics are also merging in new areas, for example: (a) using ML for general circulation model parametrization, (b) adding physics constraints in ML models, and (c) using ML in data assimilation. Impact Statement This perspective paper reviews the evolution and growth of machine learning (ML) models in environmental science. The opaque nature of ML models led to decades of slow growth, but exponential growth commenced around the mid-2010s. Novel ML models which have contributed to this exponential growth (e.g., deep convolutional neural networks, encoder-decoder networks, and generative-adversarial networks) are reviewed, as well as approaches to merging ML models with physics-based models.
图像标注任务是人工智能领域中将机器视觉(Computer Vision)与自然语言处理(Natural Language Processing)两大方向相结合的任务,受到学界极大的关注。本文针对目前主流的图像描述算法进行综合的研究,基于目前图像标注任务中取得优秀效...
详细信息
图像标注任务是人工智能领域中将机器视觉(Computer Vision)与自然语言处理(Natural Language Processing)两大方向相结合的任务,受到学界极大的关注。本文针对目前主流的图像描述算法进行综合的研究,基于目前图像标注任务中取得优秀效果的CNN-LSTM描述生成算法,引入目前机器视觉方向上取得长足发展的目标检测框架Faster R-CNN作编码器替换CNN,使用图像区域特征输入解码器;在解码器部分的循环神经网络中使用注意力机制,进一步强化区域图像特征对解码器生成自然语言描述的贡献,从而构成从区域特征到全局描述的结构化图像标注框架。这一图像标注算法在MSCO⁃CO数据集上进行训练与测试(分别在训练集与测试集上进行),我们提出的模型获得了超过了基线模型的效果。
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