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9 October 2023 Image classification model based on self-supervised deep learning
Jiankun Xiao
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Proceedings Volume 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023); 127911Y (2023) https://doi.org/10.1117/12.3004961
Event: Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 2023, Qingdao, SD, China
Abstract
Image classification methods are extremely devised to dispose the predictions of images labels through extracting the features. Existing methods mainly utilize unsupervised deep learning method, which will cause numerous computation costs and require human to label the images data set. Therefore, we propose a novel self-supervised deep learning method to solve the image classification issue. Initially, we extract the feature vectors of input data through utilizing a residual network. Subsequently, the self-supervised framework is established with a loss function to optimize the training results. At last, we simulate our proposed method on CIFAR-10 and MNIST classification tasks data set to evaluate the performance and compare our method with existing unsupervised learning methods. From our continuous experimental results and comparison simulations, we can conclude that our devised method can achieve the classification tasks for various input images with reasonable computation costs.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Jiankun Xiao "Image classification model based on self-supervised deep learning", Proc. SPIE 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 127911Y (9 October 2023); https://doi.org/10.1117/12.3004961
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KEYWORDS
Image classification
Machine learning
Deep learning
Image processing
Feature extraction
Computer vision technology
Visual process modeling
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