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IEEE/ICACT20260373 Slide.06        [Big Slide]       [YouTube]	Oral Presentation
	To bridge this gap, we propose a GAN-Based Recognition Model that operates as a minimax game between a Generator and a Discriminator. The Generator synthesizes realistic "fake" data to augment our training set, while the Discriminator performs a dual function: it not only evaluates the authenticity of the data in an unsupervised manner but also performs supervised classification to identify terrain types like soil, grass, and water.

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