Critical Synthesis
Critical synthesis in various fields focuses on generating realistic and diverse data, often using machine learning models to overcome limitations of real-world datasets. Current research emphasizes the development and application of diffusion models, generative adversarial networks (GANs), and transformer-based architectures for tasks ranging from image and speech synthesis to molecular design and controller synthesis. This work is significant for expanding datasets in data-scarce domains, improving the performance and robustness of AI systems, and enabling new applications in medicine, materials science, and beyond.
Papers
Painting 3D Nature in 2D: View Synthesis of Natural Scenes from a Single Semantic Mask
Shangzhan Zhang, Sida Peng, Tianrun Chen, Linzhan Mou, Haotong Lin, Kaicheng Yu, Yiyi Liao, Xiaowei Zhou
Point Cloud Registration for LiDAR and Photogrammetric Data: a Critical Synthesis and Performance Analysis on Classic and Deep Learning Algorithms
Ningli Xu, Rongjun Qin, Shuang Song
Text-Guided Scene Sketch-to-Photo Synthesis
AprilPyone MaungMaung, Makoto Shing, Kentaro Mitsui, Kei Sawada, Fumio Okura