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Recent studies show that paddings in convolutional neural networks encodeabsolute position information which can negatively affect the model performancefor certain tasks. However, existing metrics for quantifying the strength ofpositional information remain unreliable and frequently lead to erroneousresults. To address this issue, we propose novel metrics for measuring (andvisualizing) the encoded positional information. We formally define the encodedinformation as PPP (Position-information Pattern from Padding) and conduct aseries of experiments to study its properties as well as its formation. Theproposed metrics measure the presence of positional information more reliablythan the existing metrics based on PosENet and a test in F-Conv. We alsodemonstrate that for any extant (and proposed) padding schemes, PPP isprimarily a learning artifact and is less dependent on the characteristics ofthe underlying padding schemes.

Unveiling The Mask of Position-Information Pattern Through the Mist of Image Features