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IEEE/ICACT20260070 Slide.18        [Big Slide]	Oral Presentation
	GIDLMM was compared against the state-of-the-art "CLIPping" model on GAN datasets. While the CLIPping "Adapter Network" performs slightly better when trained on massive datasets of 100,000 images, GIDLMM remains highly competitive using only 1,000 training samples . Crucially, when compared to the CLIPping "Linear Probing" method, GIDLMM significantly outperforms it across all dataset sizes, achieving much higher average accuracy . This highlights the models superior data efficiency and ability to learn effective features even in low-resource scenarios.

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