A*-Inspired Batch Selection Improves CNN Training Efficiency and Performance
Researchers introduce A*-Inspired Batch Selection (A*-BS), a lightweight, model-agnostic method that schedules mini-batches using a heuristic search to enhance convergence and maintain a strong learning signal during CNN training. On the MedMNIST-v2 benchmark, a simple CNN using A*-BS outperformed ResNet-18 and ResNet-50 on half of the twelve tasks, achieving up to 15% higher accuracy and AUC, and trained substantially faster. The method integrates into existing pipelines without modifying architectures or optimizers.
Why it matters: This work demonstrates that intelligent batch ordering can partially offset the need for deeper networks, providing a computationally efficient alternative for CNN training.
Full story at: arXiv Machine Learning ↗