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.

该研究引入了一种新的专家生成方法，它通过将顺序处理通用低级特征与高级特征的并行和嵌套结合起来的新的分层分类网络拓扑结构，实现了动态降低任务和计算复杂度的能力。该方法在资源受限条件下能够选择只有任务相关类别的高级特征，可以显著减少推理成本，对于轻量级和可适应性的网络设计具有前瞻性意义。在动态推理方面，我们的方法可以排除高达 88.7% 的参数和 73.4% 的 GMAC 操作，与比较基准相比，在所评估的案例中，平均减少了 47.6% 的参数和 5.8% 的 GMAC 操作。