深度卷积神经网络的滤波器其实更通用，而非越来越专一。
The Master Key Filters Hypothesis: Deep Filters Are General
- 发现深层卷积滤波器在多个数据集上仍保持通用性。
- 跨数据集迁移时，冻结深层滤波器表现良好且可进一步提升。
- 适合关注模型泛化与迁移学习的研究者阅读。
本文挑战了卷积神经网络（CNN）深层滤波器趋于专用化的主流观点。基于在 ImageNet 上训练的深度可分离卷积神经网络（DS-CNN）中观察到的可聚类重复模式，我们将其分析扩展至多种领域和数据集。对 DS-CNN 的研究表明，深层滤波器维持通用性，与预期的类别特异性转变相反。通过迁移学习实验验证，来自不同数据集训练模型的冻结滤波器表现优异，且来自更大数据集的滤波器效果更佳。研究结果表明，深度可分离卷积所学的空间特征在所有层级、领域和架构中均保持通用性。该工作为理解神经网络泛化机制提供了新视角，尤其对迁移学习与模型设计具有重要启示。
原文摘要 · Abstract (English)
This paper challenges the prevailing view that convolutional neural network (CNN) filters become increasingly specialized in deeper layers. Motivated by recent observations of clusterable repeating patterns in depthwise separable CNNs (DS-CNNs) trained on ImageNet, we extend this investigation across various domains and datasets. Our analysis of DS-CNNs reveals that deep filters maintain generality, contradicting the expected transition to class-specific filters. We demonstrate the generalizability of these filters through transfer learning experiments, showing that frozen filters from models trained on different datasets perform well and can be further improved when sourced from larger datasets. Our findings indicate that spatial features learned by depthwise separable convolutions remain generic across all layers, domains, and architectures. This research provides new insights into the nature of generalization in neural networks, particularly in DS-CNNs, and has significant implications for transfer learning and model design.
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