论文标题
分辨率自适应网络以有效推理
Resolution Adaptive Networks for Efficient Inference
论文作者
论文摘要
自适应推断是在深网络中的准确性和计算成本之间实现动态折衷的有效机制。现有作品主要利用网络深度或宽度中的体系结构冗余。在本文中，我们专注于输入样品的空间冗余，并提出了一种新颖的分辨率自适应网络（RANET），该网络的灵感来自直觉，即低分辨率表示足以分类包含具有原类特征的大型对象的“简单”输入，而仅“一定”样品需要空间详细的信息。在RANET中，首先将输入图像路由到轻巧的子网络，该子网络有效提取低分辨率表示形式，并且那些具有高预测置信度的样本将尽早退出网络而不会进一步处理。同时，网络中的高分辨率路径保持识别“硬”样本的能力。因此，RANET可以有效地减少推断高分辨率输入所涉及的空间冗余。从经验上讲，我们在任何时间预测设置和预算的批处理分类设置中都在CIFAR-10，CIFAR-100和IMAGENET数据集上证明了拟议的RANET的有效性。
Adaptive inference is an effective mechanism to achieve a dynamic tradeoff between accuracy and computational cost in deep networks. Existing works mainly exploit architecture redundancy in network depth or width. In this paper, we focus on spatial redundancy of input samples and propose a novel Resolution Adaptive Network (RANet), which is inspired by the intuition that low-resolution representations are sufficient for classifying "easy" inputs containing large objects with prototypical features, while only some "hard" samples need spatially detailed information. In RANet, the input images are first routed to a lightweight sub-network that efficiently extracts low-resolution representations, and those samples with high prediction confidence will exit early from the network without being further processed. Meanwhile, high-resolution paths in the network maintain the capability to recognize the "hard" samples. Therefore, RANet can effectively reduce the spatial redundancy involved in inferring high-resolution inputs. Empirically, we demonstrate the effectiveness of the proposed RANet on the CIFAR-10, CIFAR-100 and ImageNet datasets in both the anytime prediction setting and the budgeted batch classification setting.
