Deep convolutional neural networks achieve remarkable performance by exhaustively processing dense spatial feature maps, yet this brute-force strategy introduces significant computational redundancy and encourages reliance on spurious background correlations. As a result, modern vision models remain brittle and difficult to interpret. We propose Energy-Regularized Spatial Masking (ERSM), a novel framework that reformulates feature selection as a differentiable energy minimization problem. By embedding a lightweight Energy-Mask Layer inside standard convolutional backbones, each visual token is assigned a scalar energy composed of two competing forces: an intrinsic Unary importance cost and a Pairwise spatial coherence penalty. Unlike prior pruning methods that enforce rigid sparsity budgets or rely on heuristic importance scores, ERSM allows the network to autonomously discover an optimal information-density equilibrium tailored to each input. We validate ERSM on convolutional architectures and demonstrate that it produces emergent sparsity, improved robustness to structured occlusion, and highly interpretable spatial masks, while preserving classification accuracy. Furthermore, we show that the learned energy ranking significantly outperforms magnitude-based pruning in deletion-based robustness tests, revealing ERSM as an intrinsic denoising mechanism that isolates semantic object regions without pixel-level supervision.

翻译：深度卷积神经网络通过穷举处理密集空间特征图实现了卓越的性能，但这种暴力策略引入了显著的计算冗余，并助长了对虚假背景关联的依赖。因此，现代视觉模型仍显脆弱且难以解释。我们提出能量正则化空间掩码（Energy-Regularized Spatial Masking, ERSM），该框架将特征选择重新定义为可微能量最小化问题。通过在标准卷积骨干网络中嵌入轻量级能量掩码层，每个视觉标记被赋予由两种竞争性作用力组成的标量能量：内在的一元重要性代价与成对空间一致性惩罚。不同于早期采用刚性稀疏预算或依赖启发式重要性分数的剪枝方法，ERSM允许网络自主发现针对每个输入优化的信息密度均衡。我们在卷积架构上验证了ERSM，证明其能产生涌现稀疏性、增强对结构遮挡的鲁棒性、形成高度可解释的空间掩码，同时保持分类精度。进一步研究表明，在基于删除的鲁棒性测试中，学习到的能量排序显著优于基于幅度的剪枝，揭示ERSM作为一种内在去噪机制，无需像素级监督即可隔离语义目标区域。