ImageNet LT

ImageNet-LT is a benchmark dataset used to evaluate the performance of image classification models on long-tailed distributions, where some classes have far fewer examples than others. Current research focuses on improving model robustness to this imbalance through techniques like data augmentation (including synthetic data generation guided by classifier feedback), novel loss functions (e.g., incorporating contrastive learning and logits retargeting), and architectural modifications (e.g., employing multiple experts or dynamic channel selection). Addressing the long-tailed problem is crucial for building more reliable and generalizable machine learning systems, particularly in real-world applications where data is often inherently imbalanced.