Large Scale ImageNet

Large-scale ImageNet research focuses on leveraging this massive dataset to advance deep learning capabilities, primarily in image classification and related tasks. Current research emphasizes improving model robustness to noisy labels, handling out-of-distribution data (like scaled images), and developing efficient continual learning strategies, often employing transformer architectures and techniques like deep ensembles and dynamic token expansion. These advancements are crucial for building more reliable and adaptable AI systems with applications ranging from structural damage detection to geospatial analysis, impacting various fields beyond computer vision.