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Graph Neural Networks with Prototype Nodes for Few-shot Image Classification
http://doi.org/10.5626/JOK.2023.50.2.127
The remarkable performance of deep learning models is based on a large amount of training data. However, there are a number of domains where it is difficult to obtain such a large amount of data, and in these domains a large amount of resources must be invested for data collection and refining. To overcome these limitations, research on few-shot learning, which enables learning with only a small number of data, is being actively conducted. In particular, among meta learning methodologies, metric-based learning which utilizes similarity between data has the advantage that it does not require fine-tuning of the model for a new task, and recent studies using graph neural networks have shown good results. A few-shot classification model based on a graph neural network can explicitly process data characteristics and the relationship between data by constructing a task graph using data of a given support set and query set as nodes. The EGNN(Edge-labeling Graph Neural Net) model expresses the similarity between data in the form of edge labels and models the intra-class and inter-class similarity more clearly. In this paper, we propose a method of applying a prototype node representing each class to few-shot task graph to model the similarity between data and class-data at the same time. The proposed model provides a generalized prototype node that is created based on task data and class configuration, and it can perform two different few-shot image classification predictions based on the prototype-query edge label or the Euclidean distance between prototype-query nodes. Comparing the 5-way 5-shot classification performance on the mini-ImageNet dataset with the EGNN model and other meta-learning-based few-shot classification models, the proposed model showed significant performance improvement.
Improving Low Resource Chest X-ray Classification Accuracy by Combining Data Augmentation and Weakly Supervised Learning
http://doi.org/10.5626/JOK.2021.48.9.1027
Deep learning-based medical image analysis technology has been developed to the extent that it shows an accuracy surpassing the ability of a human radiologist. However, labeling sample data for use in learning medical images requires human experts and a great deal of time and expense. In addition, the training data for medical images has an unbalanced data distribution in many cases. For example, in the case of the ChestX-ray14 dataset, the difference between the number of data for infiltration and hernia is about 87 times. In this study, we proposed a method that combines the data augmentation algorithm Mixup and weakly supervised learning to improve the performance of data-imbalanced chest X-ray classification. The proposed method is to apply Mixup algorithm to a small number of labeled data and a large number of unlabeled data to alleviate data imbalance and performs curriculum learning that effectively utilizes the unlabeled data while cycling through the teacher model and the student model. Experimental results in an environment with a small number of labeled data and a large number of unlabeled data that can be considered in the medical field showed that the classification performance was improved by combining data augmentation and weakly supervised learning and that the cyclic curriculum learning was effective.
Spammer Detection using Features based on User Relationships in Twitter
Twitter is one of the most famous SNS(Social Network Service) in the world. Twitter spammer accounts that are created easily by E mail authentication deliver harmful content to twitter users. This paper presents a spammer detection method that utilizes features based on the relationship between users in twitter. Relationship based features include friends relationship that represents user preferences and type relationship that represents similarity between users. We compared the performance of the proposed method and conventional spammer detection method on a dataset with 3% to 30% spammer ratio, and the experimental results show that proposed method outperformed conventional method in Naive Bayesian Classification and Decision Tree Learning.
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