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In the field of Computer Vision the text-to-image synthesis is a challenging task involving the generation of realistic images from textual descriptions. Deep Convolutional Generative Adversarial Networks (DCGANs) have emerged as a powerful framework for addressing this problem. DCGANs, originally designed for image generation, consist of a generator and a discriminator network. The generator employs transposed convolutional layers to transform random noise vectors into realistic images, while the discriminator learns to differentiate between real images and those generated by the generator. DC GANs are generally more efficient in terms of training speed. DCGANs utilize convolutional layers in both the generator and the discriminator networks for text-to-image synthesis,the approach in it incorporates textual descriptions as conditioning information during image generation. By using text embeddings with the generator's input, the model learns to generate images that capture the labels of the provided text. This textual conditioning guides the generation process, resulting in images that are coherent with the given textual input.
Keywords:
Computer Vision, Deep learning, Deep Convolutional GAN(DC GAN), Convolutional Layers, Realistic images, Text to image generation
Cite Article:
"Exploring Realistic Image Synthesis using Deep Convolutional GANs", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.9, Issue 4, page no.650 - 657, April-2024, Available :http://www.ijrti.org/papers/IJRTI2404091.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator