Abstract
Recent years have witnessed growing interests in designing efficient neural networks and neural architecture search (NAS). Although remarkable efficiency and accuracy have been achieved, existing expert designed and NAS models neglect the fact that input instances are of varying complexity thus different amount of computation is required. Inference with a fixed model that processes all instances through the same transformations would waste plenty of computational resources. Therefore, customizing the model capacity in an instance-aware manner is highly demanded. To address this issue, we propose an Instance-aware Selective Branching Network-ISBNet, which supports efficient instance-level inference by selectively bypassing transformation branches of insignificant importance weight. These weights are determined dynamically by accompanying lightweight hypernetworks SelectionNets and further recalibrated by gumbel-softmax for sparse branch selection. Extensive experiments show that ISBNet achieves extremely efficient inference in terms of parameter size and FLOPs comparing to existing networks. For example, ISBNet takes only 8.03% parameters and 30.60% FLOPs of the state-of-the-art efficient network ShuffleNetV2 with comparable accuracy.
Abstract (translated)
近年来，人们对设计有效的神经网络和神经结构搜索（NAS）的兴趣日益浓厚。虽然已经取得了显著的效率和准确性，但现有的专家设计和NAS模型忽略了输入实例具有不同复杂性的事实，因此需要不同的计算量。使用固定模型通过相同的转换处理所有实例的推理将浪费大量的计算资源。因此，迫切需要以实例感知的方式定制模型容量。为了解决这个问题，我们提出了一个实例感知的选择性分支网络isbnet，它通过选择性地绕过不重要权重的转换分支来支持有效的实例级推理。这些权重由伴随的轻量级超网络选择网络动态确定，并由Gumbel Softmax为稀疏分支选择进一步重新校准。大量实验表明，与现有网络相比，isbnet在参数大小和触发器方面实现了非常有效的推理。例如，ISBNET仅采用8.03%的参数和30.60%的最先进的高效网络shufflenetv2，具有相当的准确性。
URL
https://arxiv.org/abs/1905.04849
