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Zero-small sample classification method with model structure self-optimization and its application in capability evaluation
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DOI：10.1007/s10489-021-02686-8.png)
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To solve the problems of limited expression of feature information in a discrete evaluation sequence, the high cost of model optimization, and insufficient discrimination information to detect missing classes, a zero-small sample capability evaluation (ZES) method based on model structure self-optimization is proposed. Spatial transformation of the gram angle field was used to optimize the information richness and data correlation of the sample sequences. A multi-mechanism fusion strategy (MFS) was designed for self-optimization of the visual feature extraction model. Through self-adaptive global optimization of ten key parameters in the model, a computational model suitable for small samples and with self-learning ability was constructed. Sample semantic attributes and a visual-semantic feature mapping were constructed, and the unseen class labels were predicted according to cosine similarity. The experimental results verify the feasibility and effectiveness of the proposed method in the aspects of model adaptive optimization and evaluation of unseen samples and provide a new idea for constructing a comprehensive evaluation, prediction, and diagnosis system and method design in related fields.
Keyword:
Zero-small sample
Capability evaluation
Self-optimization
Multi-mechanism fusion strategy (MFS)
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