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November 21, 2025Applied Computing and InformaticsOpen Access

Image generation based on image description using artificial intelligence

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Authors

AŠAndrej ŠimićMBMarina Bagić Babac

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Overview

Explores generative adversarial networks for image generation from text descriptions, suggesting improved quality and coherence.

Key Points

  • Generated high-quality images aligned with textual descriptions, enhancing visual coherence and diversity.
  • Research implemented and trained generative adversarial networks with deep learning methods on multiple large datasets.
  • Performance metrics like inception score and Fréchet inception distance evaluated the effectiveness of the proposed models.
  • Findings suggest promising advancements in multimodal content generation techniques for future research.

Cite This Study

Šimić et al. (2025) studied this question.

synapsesocial.com/papers/6924e3f2c0ce034ddc34f03ehttps://doi.org/10.1108/aci-05-2025-0186
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Towards Text Contextual Understanding: Text Feature Fusion GAN for Text-to-Image Generation2024
  2. 2MemAttn‐CL: Unified Memory, Attention, and Contrastive Learning for Enhanced Text‐to‐Image Generation2025
  3. 3Advancements in Text-to-Image Generation through Generative AI2024 · 2 citations
  4. 4Enhancing Image Realism Through Fine Grained Text to Image Synthesis2024
  5. 5Text-to-image generation with enhanced GANs: Bridging semantic gaps using RNN and CNN.2026 · 1 citations