There exists a distribution discrepancy between training and testing, in the
way images are fed to modern CNNs. Recent work tried to bridge this gap either
by fine-tuning or re-training the network at different resolutions. However
re-training a network is rarely cheap and not always viable. To this end, we
propose a simple solution to address the train-test distributional shift and
enhance the performance of pre-trained models -- which commonly ship as a
package with deep learning platforms \eg, PyTorch. Specifically, we demonstrate
that running inference on the center crop of an image is not always the best as
important discriminatory information may be cropped-off. Instead we propose to
combine results for multiple random crops for a test image. This not only
matches the train time augmentation but also provides the full coverage of the
input image. We explore combining representation of random crops through
averaging at different levels \ie, deep feature level, logit level, and softmax
level. We demonstrate that, for various families of modern deep networks, such
averaging results in better validation accuracy compared to using a single
central crop per image. The softmax averaging results in the best performance
for various pre-trained networks without requiring any re-training or
fine-tuning whatsoever. On modern GPUs with batch processing, the paper's
approach to inference of pre-trained networks, is essentially free as all
images in a batch can all be processed at once.

本文提出了一种简单的方法来解决训练和测试集之间的分布差异，通过在测试图像上使用多个随机裁剪的结果，并将其进行平均来提高预训练模型的性能。使用 softmax 平均可在不需要重新训练的情况下获得各种预训练网络的最佳性能。