  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
