The global generative AI in content creation market is expected to see exponential growth in the coming years, driven by advancements in machine learning…
Browsing: Generative Adversarial Networks
This article discusses the limitations of traditional image tokenization methods and introduces a new approach, Transformer-based 1-Dimensional Tokenizer (TiTok), which utilizes a 1D latent…
This article discusses the use of deep neural networks (DNNs) and a novel data augmentation method based on generative adversarial networks (GANs) to improve…
AI has evolved from a concept in science fiction to a serious factor in various industries, with its ability to generate logos and images…
This article explores some of the most influential deep learning architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs),…
Synthetic data is artificial material that can be created by computer programs and AI tools, with images and textual information being the most feasible…
Recent advances in machine learning have greatly impacted quantitative finance, with techniques such as deep learning, reinforcement learning, and generative adversarial networks being used…
This article discusses the advancements and applications of deep learning techniques in the field of computer vision. It highlights the success of methods such…
Applications of GANs GANs have found widespread use in various industries, including image generation, text-to-image translation, and video generation. They have also been used…
The integration of Artificial Intelligence (AI) technology in the visual arts is revolutionizing the art world. Through generative adversarial networks (GANs), machines are able…