Text-To-Image Generation Using Generative Adversarial Network
Harsh Sawaji, Musab Shaikh, Aayush Shah, Sumitra Sadhukhan
The creation of images is a process that can be achieved using various methods, including manual techniques such as painting and drawing, or digital methods such as computer-generated graphics or image generation using deep learning models. In recent years, there has been a surge of interest in using deep learning algorithms, particularly generative models, to generate realistic and high-quality images. One such model is the Generative Adversarial Network (GAN), which is a deep learning architecture composed of two neural networks: a generator and a discriminator. The generator creates synthetic data (images) from random noise, while the discriminator's task is to distinguish between the synthetic data and real data. The two networks are trained together in a zero-sum game, where the generator aims to produce synthetic data that is indistinguishable from the real data, while the discriminator tries to correctly identify the synthetic data. Through this adversarial training process, GANs can generate high-quality synthetic data that can be used for a variety of applications, including image and video generation, data augmentation, and domain adaptation. GANs have shown impressive results in various fields, but they also pose some challenges, such as mode collapse, where the generator produces a limited set of outputs and instability during training.
Article Details
Unique Paper ID: 159199

Publication Volume & Issue: Volume 9, Issue 11

Page(s): 670 - 675
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