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Adversarial Kinetic Prototype Framework for Open Set Recognition
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AI
DOI：10.1109/TNNLS.2022.3231924.png)
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摘要
En 中文
Due to the complexity of real-world applications, open set recognition is often more practical than closed set recognition. Compared with closed set recognition, open set recognition needs not only to recognize known classes but also to identify unknown classes. Different from most of the current methods, we proposed three novel frameworks with kinetic pattern to address the open set recognition problems, and they are kinetic prototype framework (KPF), adversarial KPF (AKPF), and an upgraded version of the AKPF, AKPF++. First, KPF introduces a novel kinetic margin constraint radius, which can improve the compactness of the known features to increase the robustness for the unknowns. Based on KPF, AKPF can generate adversarial samples and add these samples into the training phase, which can improve the performance with the adversarial motion of the margin constraint radius. Compared with AKPF, AKPF++ further improves the performance by adding more generated data into the training phase. Extensive experimental results on various benchmark datasets indicate that the proposed frameworks with kinetic pattern are superior to other existing approaches and achieve the state-of-the-art performance.
Keyword:
Prototypes
Kinetic theory
Training
Data models
Optimization
Feature extraction
Neural networks
Adversarial kinetic prototype framework (AKPF)
adversarial motion
kinetic pattern
open set recognition
期刊
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8.9
论文数:
7.6K
被引数:
7.2W
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