人工智能
特征(语言学)
计算机科学
模式识别(心理学)
特征向量
欧几里德距离
特征学习
水准点(测量)
代表(政治)
集合(抽象数据类型)
机器学习
哲学
地理
法学
程序设计语言
大地测量学
政治
语言学
政治学
作者
Zhunga Liu,Yimin Fu,Quan Pan,Zuowei Zhang
标识
DOI:10.1109/tpami.2022.3227913
摘要
Open set recognition (OSR) aims to correctly recognize the known classes and reject the unknown classes for increasing the reliability of the recognition system. The distance-based loss is often employed in deep neural networks-based OSR methods to constrain the latent representation of known classes. However, the optimization is usually conducted using the nondirectional euclidean distance in a single feature space without considering the potential impact of spatial distribution. To address this problem, we propose orientational distribution learning (ODL) with hierarchical spatial attention for OSR. In ODL, the spatial distribution of feature representation is optimized orientationally to increase the discriminability of decision boundaries for open set recognition. Then, a hierarchical spatial attention mechanism is proposed to assist ODL to capture the global distribution dependencies in the feature space based on spatial relationships. Moreover, a composite feature space is constructed to integrate the features from different layers and different mapping approaches, and it can well enrich the representation information. Finally, a decision-level fusion method is developed to combine the composite feature space and the naive feature space for producing a more comprehensive classification result. The effectiveness of ODL has been demonstrated on various benchmark datasets, and ODL achieves state-of-the-art performance.
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