🤖 AI Summary
Traditional CNN training relies on random mini-batch sampling, which often leads to rapid saturation of learning signals as most samples quickly become “easy,” thereby slowing convergence. This work proposes A*-inspired Batch Selection (A*-BS), the first approach to integrate A* search into batch selection, introducing a dynamic scoring mechanism that jointly considers sample loss difficulty and reuse penalties to adaptively select informative and diverse batches. Without modifying network architecture or optimizer, A*-BS achieves superior performance on lightweight CNNs across half of the MedMNIST-v2 benchmark tasks, outperforming ResNet-18/50 in both accuracy and AUC—by up to 15% relatively—while significantly accelerating training. These results demonstrate that intelligent batch sequencing can partially substitute for model depth.
📝 Abstract
Common practice when training Convolutional Neural Networks (CNNs) is to use randomly shuffled mini-batches. This creates two limitations: slower convergence, and a diminishing learning signal, since many samples are quickly classified as easy during training. We address these inefficiencies with A*-Inspired Batch Selection (A*-BS), a lightweight, model-agnostic strategy that formulates mini-batch scheduling as a heuristic search problem. Each batch is treated as a node in a search space and ranked using an A*-like score combining a loss-based difficulty measure with a reuse penalty. This encourages informative gradient updates and batch diversity throughout training, without modifying network architectures or optimization algorithms, so it integrates seamlessly into existing pipelines. We evaluate A*-BS on the twelve 2D classification tasks of the MedMNIST-v2 benchmark, using a deliberately simple architecture of approximately 2.25x10^5 parameters, compared against the ResNet-18 and ResNet-50 baselines reported by the benchmark. On half of these tasks, the lightweight model with A*-BS reaches higher accuracy and AUC than both ResNet baselines, with relative gains of up to 15%. An ablation under identical architecture and hyperparameters shows A*-BS outperforms random batch shuffling on all twelve tasks. Wall-clock measurements further show the lightweight CNN with A*-BS trains substantially faster than ResNet-18 and ResNet-50 on identical hardware. These results indicate that intelligent batch ordering can partially compensate for reduced architectural complexity, offering a computationally efficient alternative to deeper models, with reliability reinforced by strong performance even against deeper, more sophisticated architectures.