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A Survey of Advantages of Self-Supervised Learning Models in Visual Recognition Tasks
Euihyun Yoon, Hyunjong Lee, Donggeon Kim, Joochan Park, Jinkyu Kim, Jaekoo Lee
http://doi.org/10.5626/JOK.2024.51.7.609
Recently, the field of teacher-based artificial intelligence (AI) has been rapidly advancing. However, teacher-based learning relies on datasets with specified correct answers, which can increase the cost of obtaining these correct answers. To address this issue, self-supervised learning, which can learn general features of photos without needing correct answers, is being researched. In this paper, various self-supervised learning models were classified based on their learning methods and backbone networks. Their strengths, weaknesses, and performances were then compared and analyzed. Photo classification tasks were used for performance comparison. For comparing the performance of transfer learning, detailed prediction tasks were also compared and analyzed. As a result, models that only used positive pairs achieved higher performance by minimizing noise than models that used both positive and negative pairs. Furthermore, for fine-grained predictions, methods such as masking images for learning or utilizing multi-stage models achieved higher performance by additionally learning regional information.
A Contrastive Learning Method for Automated Fact-Checking
Seonyeong Song, Jejun An, Kunwoo Park
http://doi.org/10.5626/JOK.2023.50.8.680
As proliferation of online misinformation increases, the importance of automated fact-checking, which enables real-time evaluation, has been emphasized. In this study, we propose a contrastive learning method for automated fact-checking in Korean. The proposed method deems a sentence similar to evidence as a positive sample to determine the authenticity of a given claim. In evaluation experiments, we found that the proposed method was more effective in the sentence selection step of finding evidence sentences for a given claim than previous methods. such as a finetuned pretrained language model and SimCSE. This study shows a potential of contrastive learning for automated fact-checking.
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