Synthetic Data
Synthetic data generation aims to create artificial datasets that mimic the statistical properties of real-world data, addressing limitations like data scarcity, privacy concerns, and high annotation costs. Current research focuses on developing sophisticated generative models, including generative adversarial networks (GANs), energy-based models (EBMs), diffusion models, and masked language models, tailored to various data types (images, text, tabular data, audio). This rapidly evolving field significantly impacts diverse scientific domains and practical applications by enabling the training of robust machine learning models in situations where real data is insufficient or ethically problematic, ultimately improving model performance and expanding research possibilities.
Papers
Neural-Sim: Learning to Generate Training Data with NeRF
Yunhao Ge, Harkirat Behl, Jiashu Xu, Suriya Gunasekar, Neel Joshi, Yale Song, Xin Wang, Laurent Itti, Vibhav Vineet
PieTrack: An MOT solution based on synthetic data training and self-supervised domain adaptation
Yirui Wang, Shenghua He, Youbao Tang, Jingyu Chen, Honghao Zhou, Sanliang Hong, Junjie Liang, Yanxin Huang, Ning Zhang, Ruei-Sung Lin, Mei Han
Hybrid CNN-Transformer Model For Facial Affect Recognition In the ABAW4 Challenge
Lingfeng Wang, Haocheng Li, Chunyin Liu
Learning from Synthetic Data: Facial Expression Classification based on Ensemble of Multi-task Networks
Jae-Yeop Jeong, Yeong-Gi Hong, JiYeon Oh, Sumin Hong, Jin-Woo Jeong, Yuchul Jung
BYEL : Bootstrap Your Emotion Latent
Hyungjun Lee, Hwangyu Lim, Sejoon Lim
Facial Affect Analysis: Learning from Synthetic Data & Multi-Task Learning Challenges
Siyang Li, Yifan Xu, Huanyu Wu, Dongrui Wu, Yingjie Yin, Jiajiong Cao, Jingting Ding
Category-Level 6D Object Pose and Size Estimation using Self-Supervised Deep Prior Deformation Networks
Jiehong Lin, Zewei Wei, Changxing Ding, Kui Jia
TabSynDex: A Universal Metric for Robust Evaluation of Synthetic Tabular Data
Vikram S Chundawat, Ayush K Tarun, Murari Mandal, Mukund Lahoti, Pratik Narang