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A U-Net based Self-Supervised Image Generation Model Applying PCA using Small Datasets

Published: 29 May 2023 Publication History

Abstract

Generative Adversarial Networks (GAN) is a research-based on deep learning technology that synthetically generates, combines, and transforms images similar to the original images. The main focus of GAN existing work has been to improve the quality of generated images and to generate high-resolution images by changing the training scheme or devising more complex models. However, these models require a large amount of data and are not suitable for training with a small amount of data. To address these challenges, this paper aims to improve the quality of images and the stability of training with a small dataset by proposing a novel training method for generating real-world images by using PCA and Self-Supervised GAN. Previously, PCA was applied to DCGAN to generate images with a small dataset, but some images showed poor results. By preparing quantitatively different datasets, we show that the quality of generated image with a small dataset is equivalent, or even better when compared to the quality of the image generated with a large dataset.

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Cited By

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  • (2024)Denoising Diffusion-Based Image Generation Model Using Principal Component AnalysisIEEE Access10.1109/ACCESS.2024.350021212(170487-170498)Online publication date: 2024
  • (2024)Impact of Artificial Intelligence on the Global Economy and Technology AdvancementsArtificial General Intelligence (AGI) Security10.1007/978-981-97-3222-7_7(147-180)Online publication date: 31-Aug-2024

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        CACML '23: Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning
        March 2023
        598 pages
        ISBN:9781450399449
        DOI:10.1145/3590003
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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        New York, NY, United States

        Publication History

        Published: 29 May 2023

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        Author Tags

        1. Generative Adversarial Network
        2. Principal Component Analysis
        3. Self-Supervised Learning
        4. U-Net

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        CACML '23 Paper Acceptance Rate 93 of 241 submissions, 39%;
        Overall Acceptance Rate 93 of 241 submissions, 39%

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        • (2024)Denoising Diffusion-Based Image Generation Model Using Principal Component AnalysisIEEE Access10.1109/ACCESS.2024.350021212(170487-170498)Online publication date: 2024
        • (2024)Impact of Artificial Intelligence on the Global Economy and Technology AdvancementsArtificial General Intelligence (AGI) Security10.1007/978-981-97-3222-7_7(147-180)Online publication date: 31-Aug-2024

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