可学习调整与频谱滤波参数化的分辨率分配
Resolution Allocation with Learnable Resizing and Spectral Filter Parameterization
AI总结：
本文研究可学习下采样与频谱滤波参数化的交互，发现频谱核在分辨率分配中增益更大，在三个数据集及两种模型规模上验证了该结论，为卷积网络分辨率分配提供了新见解。
摘要：
可学习下采样和频谱域滤波器参数化分别提升了卷积网络的灵活性，但二者的交互作用尚未得到系统研究。本文通过研究空间域与频谱域学习到的卷积核，以及裁剪调整实现下的可学习下采样率，填补了这一空白。我们发现，频谱核与空间核对可学习分辨率分配的响应存在差异，其中频谱核的增益更大。在三个数据集（EuroSAT、CIFAR-10和STL-10）及两种模型规模上均观察到这一现象，在保留的测试集上，准确率增益差异为+0.013至+0.028。部分差异源于频谱核从保留更多空间区域中获得的更大增益，但在总保留区域匹配时，该差异仍为正。该模式在不同数据集和模型规模上均存在，不过其幅度及对复杂度预算的依赖随模型规模和调整实现方式变化。
英文摘要：
Learnable downsampling and spectral-domain filter parameterizations have each made convolutional networks more flexible, but their interaction has not been systematically examined. We bridge this gap by examining convolutional kernels learned in the spatial and spectral domains, together with learned downsampling ratios under a cropped resizing implementation. We find that spectral and spatial kernels respond differently to learned resolution allocation, with larger gains for the spectral kernel. We observe this consistently on three datasets (EuroSAT, CIFAR-10 and STL-10) and at two model sizes (differences in accuracy gain of +0.013 to +0.028 on held-out test sets). Part of this difference reflects the greater gain of the spectral kernel from retaining more spatial area, but the difference remains positive when total retained area is matched. This pattern is observed across datasets and model sizes, although its magnitude and dependence on the complexity budget vary with model size and the resizing implementation.
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