The wave of artificial intelligence is gradually rising, flowing into the field of education, and becoming a remedy for the “impossible triangle of education“. However, more and more studies indicate that immediate convenience may come at the cost of long-term capability. Who Is the Alarm Bell Ringing For? In 2024, Wharton School published a paper … Read more
Generative AI 2.0 Transforming From Content Generation to World Simulation Recently, the AI community has become lively again! OpenAI’s newly released video generation model Sora has truly amazed everyone, capable of generating realistic 60-second videos, as if scenes from a sci-fi movie have been brought into reality. There’s also the highly anticipated GPT-5, which, although … Read more
With the rapid development of generative AI technologies, powerful large language models like DeepSeek-R1 are at the forefront of innovation. These models, with their exceptional capabilities, bring unprecedented opportunities for enterprises and developers. However, when the DeepSeek-R1 model exposed a “thinking bias” of 14.3% in the Vectara HHEM AI hallucination test, the entire generative AI … Read more
– Digital Rebirth: In the chaos, the digital life of Nezha awakens in the code written by Taiyi Zhenren, discovering himself trapped in the virtual sandbox of Chentang Pass. This setting transforms traditional mythology into a data war on cloud servers. – Algorithmic Fate: The antivirus program written by Yuanshi Tianzun is about to format … Read more
Editor｜Debra AI Frontline Introduction: Generative Adversarial Networks (GANs) are currently widely used in various scenarios such as image generation, super-resolution image generation, image compression, image style transfer, data augmentation, and text generation. More and more researchers are engaged in the study of GAN networks, proposing various variants of GAN models, including CGAN, InfoGAN, WGAN, CycleGAN, … Read more
“ This article introduces the application of a Generative Adversarial Network in the recommendation field, namely CFGAN, along with its principles, potential issues, and solutions, and provides a code implementation and examples of running on public datasets.” Author Introduction: Zhang Xuxin, a master’s student at Huazhong University of Science and Technology, mainly researches data attack … Read more
At the request of our backend team, this issue shares the practical implementation of Generative Adversarial Networks (GANs) using MATLAB. The content mainly includes a brief introduction to GAN and its classic variants, along with relevant code examples. If you want to learn more about deep learning, feel free to message me on the backend … Read more
Author | Cheng Yu Reviewer | Zhu Yulei Today, I would like to introduce a paper jointly published in January 2020 by Bayer Crop Science, Bayer’s Machine Learning R&D Department, and the Genetic Toxicology Department in Nature Communications. This paper discusses a generative model for de novo design and synthetic optimization of molecules. The generative … Read more
Generative Adversarial Networks (GANs) are one of the most interesting and popular applications in deep learning. This article lists 10 papers on GANs that will provide you with a great introduction to GANs and help you understand the foundations of state-of-the-art techniques. The 10 selected GAN papers include: DCGANs Improved Techniques for Training GANs Conditional … Read more
Labs Introduction The generation of image data has always been a challenging task in the field of computer vision. Traditional methods for generating image data are usually based on mathematical models, making it difficult to create realistic images. With the emergence of deep neural networks and large-scale datasets, significant progress has been made in image … Read more