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Text-to-image synthesis has emerged as a pivotal domain within artificial intelligence, enabling the creation of lifelike visuals from textual descriptions. This research work introduces a novel solution to the text-to-image synthesis challenge by leveraging the Text Conditioned Auxiliary Classifier Generative Adversarial Network. The proposed TAC-GAN model is both trained and evaluated using the renowned Oxford flower dataset, a benchmark in image synthesis tasks. Notably, our TAC-GAN integrates an auxiliary classifier to ensure semantic alignment between generated images and input text descriptions. This enhancement significantly elevates the authenticity and coherence of the synthesized images, thereby enriching their overall quality. To gauge the quality and diversity of the generated images, we employ two evaluation metrics: The Inception Score and the Multi-Scale Structural Similarity (MS-SSIM) Score. The combined utilization of these metrics furnishes comprehensive insights into the perceptual excellence and structural consistency of the generated images. This holistic evaluation framework underscores the resilience and effectiveness of our approach in delivering high-quality synthesized images that faithfully correspond to textual descriptions.
Meena et al. (Mon,) studied this question.