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
Synthesis of pulses from particle detectors with a Generative Adversarial Network (GAN)
Alberto Regadío, Luis Esteban, Sebastián Sánchez-Prieto
Noise-robust zero-shot text-to-speech synthesis conditioned on self-supervised speech-representation model with adapters
Kenichi Fujita, Hiroshi Sato, Takanori Ashihara, Hiroki Kanagawa, Marc Delcroix, Takafumi Moriya, Yusuke Ijima
Cancer-Net PCa-Gen: Synthesis of Realistic Prostate Diffusion Weighted Imaging Data via Anatomic-Conditional Controlled Latent Diffusion
Aditya Sridhar, Chi-en Amy Tai, Hayden Gunraj, Yuhao Chen, Alexander Wong
DanceMeld: Unraveling Dance Phrases with Hierarchical Latent Codes for Music-to-Dance Synthesis
Xin Gao, Li Hu, Peng Zhang, Bang Zhang, Liefeng Bo
LoCo: Locally Constrained Training-Free Layout-to-Image Synthesis
Peiang Zhao, Han Li, Ruiyang Jin, S. Kevin Zhou
Enabling On-Device Large Language Model Personalization with Self-Supervised Data Selection and Synthesis
Ruiyang Qin, Jun Xia, Zhenge Jia, Meng Jiang, Ahmed Abbasi, Peipei Zhou, Jingtong Hu, Yiyu Shi
I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Models
Shiwei Zhang, Jiayu Wang, Yingya Zhang, Kang Zhao, Hangjie Yuan, Zhiwu Qin, Xiang Wang, Deli Zhao, Jingren Zhou
Efficient Bottom-Up Synthesis for Programs with Local Variables
Xiang Li, Xiangyu Zhou, Rui Dong, Yihong Zhang, Xinyu Wang