We propose a general method to train a single convolutional neural network
which is capable of switching image resolutions at inference. Thus the running
speed can be selected to meet various computational resource limits. Networks
trained with the proposed method are named Resolution Switchable Networks
(RS-Nets). The basic training framework shares network parameters for handling
images which differ in resolution, yet keeps separate batch normalization
layers. Though it is parameter-efficient in design, it leads to inconsistent
accuracy variations at different resolutions, for which we provide a detailed
analysis from the aspect of the train-test recognition discrepancy. A
multi-resolution ensemble distillation is further designed, where a teacher is
learnt on the fly as a weighted ensemble over resolutions. Thanks to the
ensemble and knowledge distillation, RS-Nets enjoy accuracy improvements at a
wide range of resolutions compared with individually trained models. Extensive
experiments on the ImageNet dataset are provided, and we additionally consider
quantization problems. Code and models are available at
this https URL

提出一种单卷积神经网络通用的方法，在推理时可以切换图像分辨率，从而可以选择满足各种计算资源限制的运行速度。用该方法训练的网络称为分辨率切换网络（RS-Nets）。通过多分辨率蒸馏和知识蒸馏，RS-Nets 在各种分辨率下的准确性得到了提高。

运行时高效图像识别的分辨率可切换网络

Resolution Switchable Networks for Runtime Efficient Image Recognition

In this paper we investigate image classification with computational resource
limits at test time. Two such settings are: 1. anytime classification, where
the network's prediction for a test example is progressively updated,
facilitating the output of a prediction at any time; and 2. budgeted batch
classification, where a fixed amount of computation is available to classify a
set of examples that can be spent unevenly across "easier" and "harder" inputs.
In contrast to most prior work, such as the popular Viola and Jones algorithm,
our approach is based on convolutional neural networks. We train multiple
classifiers with varying resource demands, which we adaptively apply during
test time. To maximally re-use computation between the classifiers, we
incorporate them as early-exits into a single deep convolutional neural network
and inter-connect them with dense connectivity. To facilitate high quality
classification early on, we use a two-dimensional multi-scale network
architecture that maintains coarse and fine level features all-throughout the
network. Experiments on three image-classification tasks demonstrate that our
framework substantially improves the existing state-of-the-art in both
settings.

该论文研究了在测试时具有计算资源限制的图像分类问题，并通过训练多个带有不同资源需求的分类器并将其作为早期退出引入单个深度卷积神经网络中以最大程度地重用计算，实现了在任意时间进行预测和分配不同的计算量来分类一组样本，框架显著改善了现有技术在两种情况下的表现。