  This study introduces a novel expert generation method that dynamically
reduces task and computational complexity without compromising predictive
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.
