论文标题
CNN滤清器DB：训练有素的卷积过滤器的实证研究
CNN Filter DB: An Empirical Investigation of Trained Convolutional Filters
论文作者
论文摘要
当前，许多理论和实际相关问题，涉及卷积神经网络（CNN）的可转移性和鲁棒性尚未解决。虽然正在进行的研究工作从各个角度吸引了这些问题，但在大多数与计算机视觉相关的情况下，这些方法可以推广到对图像数据中分配变化的影响的研究。在这种情况下，我们建议研究受过训练的CNN模型的学识称重量的变化。在这里，我们重点介绍了主要使用的3x3卷积滤波器内核的分布的性能。我们使用广泛的数据集，架构和视觉任务收集并公开提供了一个超过14亿滤波器的数据集。在拟议数据集的第一个用例中，我们可以显示许多用于实际应用的公开培训模型的高度相关属性：i）我们沿着不同轴的元素轴之间分析分配变化（或缺乏），例如，元参数轴，例如数据集，任务，架构，建筑，层或层深度的视觉类别。基于这些结果，我们得出结论，如果模型预训练可以在任意数据集符合大小和方差条件下成功。 ii）我们表明，许多预训练的模型都包含退化的过滤器，从而使其不稳定，也不适合对目标应用进行微调。 数据和项目网站：https：//github.com/paulgavrikov/cnn-filter-db
Currently, many theoretical as well as practically relevant questions towards the transferability and robustness of Convolutional Neural Networks (CNNs) remain unsolved. While ongoing research efforts are engaging these problems from various angles, in most computer vision related cases these approaches can be generalized to investigations of the effects of distribution shifts in image data. In this context, we propose to study the shifts in the learned weights of trained CNN models. Here we focus on the properties of the distributions of dominantly used 3x3 convolution filter kernels. We collected and publicly provide a dataset with over 1.4 billion filters from hundreds of trained CNNs, using a wide range of datasets, architectures, and vision tasks. In a first use case of the proposed dataset, we can show highly relevant properties of many publicly available pre-trained models for practical applications: I) We analyze distribution shifts (or the lack thereof) between trained filters along different axes of meta-parameters, like visual category of the dataset, task, architecture, or layer depth. Based on these results, we conclude that model pre-training can succeed on arbitrary datasets if they meet size and variance conditions. II) We show that many pre-trained models contain degenerated filters which make them less robust and less suitable for fine-tuning on target applications. Data & Project website: https://github.com/paulgavrikov/cnn-filter-db
