In this work, we parallelize high-level features in deep networks to
selectively skip or select class-specific features to reduce inference costs.
This challenges most deep learning methods due to their limited ability to
efficiently and effectively focus on selected class-specific features without
retraining. We propose a serial-parallel hybrid architecture with serial
generic low-level features and parallel high-level features. This accounts for
the fact that many high-level features are class-specific rather than generic,
and has connections to recent neuroscientific findings that observe spatially
and contextually separated neural activations in the human brain. Our approach
provides the unique functionality of cutouts: selecting parts of the network to
focus on only relevant subsets of classes without requiring retraining. High
performance is maintained, but the cost of inference can be significantly
reduced. In some of our examples, up to $75\,\%$ of parameters are skipped and
$35\,\%$ fewer GMACs (Giga multiply-accumulate) operations are used as the
approach adapts to a change in task complexity. This is important for mobile,
industrial, and robotic applications where reducing the number of parameters,
the computational complexity, and thus the power consumption can be paramount.
Another unique functionality is that it allows processing to be directly
influenced by enhancing or inhibiting high-level class-specific features,
similar to the mechanism of selective attention in the human brain. This can be
relevant for cross-modal applications, the use of semantic prior knowledge,
and/or context-aware processing.

通过使用串行通用的低级特征和并行的高级特征，我们的方法可以选择性地跳过或选择与类别相关的特征，从而减少推理成本。这种方法能有效地减少参数和计算复杂度，适用于移动、工业和机器人应用。