Abstract
Deep learning models are known for their ability to learn complex patterns and make accurate predictions from data, but their performance is heavily dependent on the quantity and quality of the data used to train them. In many cases, a large amount of labeled data is required to achieve high performance, but collecting and labeling data can be time-consuming and expensive. To solve these problems, this paper proposes a method to utilize active transfer learning-based image small-scale data. This allows us to learn only 74% less data on Fashion-MNIST data and derive 84.9% accuracy.
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