Gradient Alignment

Gradient alignment, a technique focusing on aligning gradients from different data sources or model objectives, aims to improve model performance and robustness. Current research explores its application in diverse areas, including domain adaptation (e.g., for medical imaging and face anti-spoofing), federated learning, and long-tailed data classification, often employing novel algorithms like dynamic gradient alignment and parallel gradient alignment. This approach holds significant promise for enhancing model generalization, mitigating biases, and improving efficiency in various machine learning tasks, particularly in scenarios with limited data or significant domain shifts.