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
GRASP-Net: Geometric Residual Analysis and Synthesis for Point Cloud Compression
Jiahao Pang, Muhammad Asad Lodhi, Dong Tian
Temporally Adjustable Longitudinal Fluid-Attenuated Inversion Recovery MRI Estimation / Synthesis for Multiple Sclerosis
Jueqi Wang, Derek Berger, Erin Mazerolle, Othman Soufan, Jacob Levman
SYNTHESIS: A Semi-Asynchronous Path-Integrated Stochastic Gradient Method for Distributed Learning in Computing Clusters
Zhuqing Liu, Xin Zhang, Jia Liu
REGAS: REspiratory-GAted Synthesis of Views for Multi-Phase CBCT Reconstruction from a single 3D CBCT Acquisition
Cheng Peng, Haofu Liao, S. Kevin Zhou, Rama Chellappa