Supervised ImageNet
Supervised ImageNet research focuses on improving image classification models by leveraging the massive ImageNet dataset. Current efforts concentrate on enhancing data curation strategies, developing more efficient training methods (including exploring alternative architectures like binary neural networks and leveraging self-supervised learning), and addressing challenges like dataset bias and the need for explainable AI. These advancements are crucial for improving the accuracy, efficiency, and trustworthiness of computer vision systems across various applications, from medical imaging to agricultural technology.
Papers
Class-Incremental Learning using Diffusion Model for Distillation and Replay
Quentin Jodelet, Xin Liu, Yin Jun Phua, Tsuyoshi Murata
Why does my medical AI look at pictures of birds? Exploring the efficacy of transfer learning across domain boundaries
Frederic Jonske, Moon Kim, Enrico Nasca, Janis Evers, Johannes Haubold, René Hosch, Felix Nensa, Michael Kamp, Constantin Seibold, Jan Egger, Jens Kleesiek
FFCV: Accelerating Training by Removing Data Bottlenecks
Guillaume Leclerc, Andrew Ilyas, Logan Engstrom, Sung Min Park, Hadi Salman, Aleksander Madry
Towards Mitigating more Challenging Spurious Correlations: A Benchmark & New Datasets
Siddharth Joshi, Yu Yang, Yihao Xue, Wenhan Yang, Baharan Mirzasoleiman