רשתות שאריות גאוסיות עם נגזרות על-קנה מידה

תקציר מקורי באנגליתarXiv:2603.02843v2 Announce Type: replace-cross Abstract: 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 scale-covariant and scale-invariant properties