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Variational Multi-Prototype Encoder for Object Recognition Using Multiple Prototype Images
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DOI：10.1109/ACCESS.2022.3151856.png)
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摘要
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In the recent research of Variational Prototyping-Encoder (VPE), the problem of classifying 2D flat objects of the unseen class has been addressed. VPE solves this problem by pre-learning the image translation task from real-world object images to their corresponding prototype images as a meta-task. VPE uses a single prototype for each object class. However, in general, a single prototype is not sufficient to represent a generic object class because the appearance can change significantly according to viewpoints and other factors. In this case, using VPE and a single prototype for each class in training can result in overfitting or performance degradation. One solution may be the use of multiple prototypes. However, this also requires costly sub-labeling for dividing the input class into smaller classes and assigning a prototype to each. Therefore, we propose a new learning method, the variational multi-prototype encoder (VaMPE), which can overcome the limitations of VPE and use multiple prototypes for each object class. The proposed method does not require additional sub-labeling other than simply adding multiple prototypes to each class. Through various experiments, we demonstrate that the proposed method outperforms VPE.
Keyword:
Prototypes
Task analysis
Training
Feature extraction
Deep learning
Perturbation methods
Neural networks
Deep learning
variational encoder
prototype learning
embedding space
image classification
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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