Research on Oracle Bone Inscription Recognition Based on Meta-Learning
In order to improve the recognition efficiency of oracle bone inscription,for the problem that there are many kinds of datasets but too few samples within the class in the process of oracle bone inscription recognition,meta-learning is introduced into the recognition of oracle bone inscription images,and a meta-learning-based oracle bone inscription recognition algorithm is proposed.Firstly,the residual network(ResNet)18 is chosen as the basic network structure,which can realize better feature extraction for oracle bone dataset.Then,the initial model parameters are learned by the meta-learning method.The experimental results show that the initial model parameters learned by this algorithm have a good effect on learning new categories for recognition,which is better than other models such as the model-agnostic meta-learning(MAML),etc.,and it is very effective for the recognition of the oracle bone dataset with few samples.This research provides a solution idea for processing and recognizing other less sample datasets.
Oracle bone inscription classificationDeep learningMeta-learningResidual network(ResNet)Convolutional neural networkModel-agnostic meta-learning(MAML)algorithm
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