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GENERATIVE ADVERSARIAL NETWORKS (GANS) FOR DATA AUGMENTATION Additionally, we present case studies across different applications, including image classification, medical imaging, and natural language processing, demonstrating the efficacy of GAN-based
GT I GAN: A Generative Adversarial Network for Data Augmentation in . . . To address this issue, we introduce GT I GAN, a novel GAN-based DA model for generating not only image samples but also their continuous ground truth vectors The core concept behind GT I GAN is the incorporation, into the RGB sample, of an extra (fourth) channel encoding the ground vec-tor
Data Augmentation Techniques Using Generative Adversarial Networks . . . Unlike the previous approaches based on transformation techniques, GANs can learn complex patterns and generate realistic synthetic data, which have virtually never been possible for fields and applications In this paper, 12 new GAN-augmentation strategies are proposed and implemented
GenAI for Synthetic Data Augmentation GANs, VAEs Diffusion Models Validating synthetic data augmentation for computer vision with measurable accuracy improvements in bird species classification This project implements and validates synthetic data augmentation for computer vision using three state-of-the-art generative modeling approaches
Using GANs for Data Augmentation - Baeldung In this article, we talked about how we can use GANs for data augmentation First, we introduced the topics of data augmentation and generative models, and then we presented conditional GANs that can be used to generate very realistic samples
Evolutionary conditional GANs for supervised data augmentation: The . . . The objective of this work is the train and use of evolutionary conditional GANs for supervised data augmentation to improve the performance of deep learning regression models in the task of assessing berry number per cluster in grapevine
[2111. 05328] Data Augmentation Can Improve Robustness In this paper, we focus on reducing robust overfitting by using common data augmentation schemes We demonstrate that, contrary to previous findings, when combined with model weight averaging, data augmentation can significantly boost robust accuracy