ImageNet Hierarchy

ImageNet hierarchy research focuses on improving the quality, efficiency, and robustness of image classification datasets and models. Current efforts concentrate on addressing inherent biases and inconsistencies within the ImageNet hierarchy, developing efficient dataset condensation techniques (like those leveraging Hebbian learning or bilevel optimization), and benchmarking model robustness against various types of corruptions, including text-guided edits. These investigations are crucial for advancing the reliability and generalizability of computer vision systems, impacting applications ranging from object recognition to adversarial machine learning.