We conduct an empirical study to test the ability of Convolutional Neural
Networks (CNNs) to reduce the effects of nuisance transformations of the input
data, such as location, scale and aspect ratio. We isolate factors by adopting
a common convolutional architecture either deployed globally on the image to
compute class posterior distributions, or restricted locally to compute class
conditional distributions given location, scale and aspect ratios of bounding
boxes determined by proposal heuristics. In theory, averaging the latter should
yield inferior performance compared to proper marginalization. Yet empirical
evidence suggests the converse, leading us to conclude that - at the current
level of complexity of convolutional architectures and scale of the data sets
used to train them - CNNs are not very effective at marginalizing nuisance
variability. We also quantify the effects of context on the overall
classification task and its impact on the performance of CNNs, and propose
improved sampling techniques for heuristic proposal schemes that improve
end-to-end performance to state-of-the-art levels. We test our hypothesis on a
classification task using the ImageNet Challenge benchmark and on a
wide-baseline matching task using the Oxford and Fischer's datasets.

本文通过使用卷积神经网络，对数据的位置、大小和宽高比等干扰因素的影响进行了实证研究，发现在当前卷积神经网络的复杂度和数据集规模的条件下，卷积神经网络对于边缘干扰变化的能力十分有限，同时还提出了改进的采样技术。作者在 ImageNet Challenge 基准分类任务和 Oxford 与 Fischer 的数据集的广泛基线匹配任务上测试了他们的假设。