This study introduces a novel expert generation method that arbitrarily reduces task and computational complexity without compromising performance. It is based on a new hierarchical classification network topology that combines sequential processing of generic low-level features with parallelism and nesting of high-level features. This structure allows for the innovative extraction technique: the ability to select only high-level features of task-relevant categories. In certain cases, it is possible to skip almost all unneeded high-level features, which can significantly reduce the inference cost and is highly beneficial in resource-constrained conditions. We believe this method paves the way for future network designs that are lightweight and adaptable, making them suitable for a wide range of applications, from compact edge devices to large-scale clouds. In terms of dynamic inference our methodology can achieve an exclusion of up to 88.7\% of parameters and 73.4\% fewer giga-multiply accumulate (GMAC) operations, analysis against comparative baselines showing an average reduction of 47,6\% in parameters and 5.8\% in GMACs across the cases we evaluated.