We propose Attentive Regularization (AR), a method to constrain the activation maps of kernels in Convolutional Neural Networks (CNNs) to specific regions of interest (ROIs). Each kernel learns a location of specialization along with its weights through standard backpropagation. A differentiable attention mechanism requiring no additional supervision is used to optimize the ROIs. Traditional CNNs of different types and structures can be modified with this idea into equivalent Targeted Kernel Networks (TKNs), while keeping the network size nearly identical. By restricting kernel ROIs, we reduce the number of sliding convolutional operations performed throughout the network in its forward pass, speeding up both training and inference. We evaluate our proposed architecture on both synthetic and natural tasks across multiple domains. TKNs obtain significant improvements over baselines, requiring less computation (around an order of magnitude) while achieving superior performance.

提出了一种名为“Attentive Regularization（AR）”的方法，通过区域关注机制来约束卷积神经网络（CNNs）中卷积核的激活图，并在不需要额外监督的情况下通过可微分的注意力机制来优化ROI。结果表明，修改后的网络（即“Targeted Kernel Networks”）在多个领域的合成和自然任务中明显优于基线模型，同时需要的计算量更少（减少了约一个数量级）。

目标核网络：具有注意正则化的更快卷积