Efficient Training of CNN Ensembles via Subgrid-Boosted Feature Selection

Convolutional Neural Networks (CNNs) have achieved remarkable success in computer vision, yet training deep architectures with millions of parameters remains computationally expensive and design-intensive. We present a novel framework for efficient optimization of CNN ensembles that integrates subgrid-boosted feature selection with boosting-inspired learning. Our method introduces subgrid selection and importance sampling to emphasize statistically informative regions of the feature space, while embedding boosting weights directly into the ensemble training process through a least squares formulation. This design accelerates convergence, reduces the burden of manual architecture tuning, and enhances predictive performance. Across multiple fine-grained image classification benchmarks, our subgrid-boosted CNN ensembles consistently outperform conventional CNNs in both accuracy and training efficiency, demonstrating the effectiveness and generality of the proposed approach.