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
Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
George Stein, Jesse C. Cresswell, Rasa Hosseinzadeh, Yi Sui, Brendan Leigh Ross, Valentin Villecroze, Zhaoyan Liu, Anthony L. Caterini, J. Eric T. Taylor, Gabriel Loaiza-Ganem
Coarse Is Better? A New Pipeline Towards Self-Supervised Learning with Uncurated Images
Ke Zhu, Yin-Yin He, Jianxin Wu
LANCE: Stress-testing Visual Models by Generating Language-guided Counterfactual Images
Viraj Prabhu, Sriram Yenamandra, Prithvijit Chattopadhyay, Judy Hoffman
Precision-Recall Divergence Optimization for Generative Modeling with GANs and Normalizing Flows
Alexandre Verine, Benjamin Negrevergne, Muni Sreenivas Pydi, Yann Chevaleyre