  We present first empirical results from our ongoing investigation of
distribution shifts in image data used for various computer vision tasks.
Instead of analyzing the original training and test data, we propose to study
shifts in the learned weights of trained models. In this work, we focus on the
properties of the distributions of dominantly used 3x3 convolution filter
kernels. We collected and publicly provide a data set with over half a billion
filters from hundreds of trained CNNs, using a wide range of data sets,
architectures, and vision tasks. Our analysis shows interesting distribution
shifts (or the lack thereof) between trained filters along different axes of
meta-parameters, like data type, task, architecture, or layer depth. We argue,
that the observed properties are a valuable source for further investigation
into a better understanding of the impact of shifts in the input data to the
generalization abilities of CNN models and novel methods for more robust
transfer-learning in this domain. Data available at:
https://github.com/paulgavrikov/CNN-Filter-DB/.
