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.
翻译:近些年来,人们在设计高效神经网络和神经结构搜索(NAS)方面的兴趣日益浓厚。虽然已经实现了显著的效率和准确性,但现有的专家设计和NAS模型忽视了以下事实:投入情况复杂程度不同,因此需要不同的计算量。如果采用固定模型,通过相同的转换处理所有情况,将浪费大量计算资源。因此,以实例识别方式定制模型能力的要求很高。为了解决这一问题,我们提议建立一个有真凭实据的选择性分流网络-ISBNet,通过选择绕过微不足道重量的转换分支,支持有效的实例级推论。这些加权由随附的轻量超网络选择网动态地确定,并由软软糖为稀疏的分支选择进一步重新校准。广泛的实验表明,ISBNet在参数大小和FLOP与现有网络相比方面极其高效的推论。例如,ISBNet仅采用8.03 %的参数和30.60%的FLOPs,其精确性可比较的状态高效网络ShuffleNet2。