Scale-invariant Gaussian derivative residual networks
Generalisation across image scales remains a fundamental challenge for deep networks, which often fail to handle images at scales not seen during training (the out-of-distribution problem). In this paper, we present provably scale-invariant Gaussian derivative residual networks (GaussDerResNets), constructed out of scale-covariant Gaussian derivative residual blocks coupled in cascade, aimed at addressing this problem. By adding residual skip connections to the previous notion of Gaussian derivative layers, deeper networks with substantially increased accuracy can be constructed, while preserving very good scale generalisation properties. Explicit proofs are provided for the underlying sc...
| Authors | Andrzej Perzanowski, Tony Lindeberg |
|---|---|
| Primary category | cs.CV |
| Published | 3 March 2026 |
| Last updated at source | 25 September 2026 |
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