Class Alignment

Class alignment in machine learning focuses on aligning feature representations across different classes or domains to improve model performance and robustness. Current research emphasizes techniques like cross-domain knowledge distillation, adversarial learning combined with self-training using predictive uncertainty, and multi-class alignment of confidence and certainty to address issues such as miscalibration and domain adaptation in object detection and pose estimation. These advancements aim to mitigate biases, improve generalization, and enhance the reliability of predictions, particularly in scenarios with imbalanced data or significant domain shifts, impacting various applications from medical image analysis to autonomous systems.