Images cannot always be expected to come in a certain standard format and
orientation. Deep networks need to be trained to take into account unexpected
variations in orientation or format. For this purpose, training data should be
enriched to include different conditions. In this study, the effects of data
enrichment on the performance of deep networks in the super resolution problem
were investigated experimentally. A total of six basic image transformations
were used for the enrichment procedures. In the experiments, two deep network
models were trained with variants of the ILSVRC2012 dataset enriched by these
six image transformation processes. Considering a single image transformation,
it has been observed that the data enriched with 180 degree rotation provides
the best results. The most unsuccessful result was obtained when the models
were trained on the enriched data generated by the flip upside down process.
Models scored highest when trained with a mix of all transformations.

研究探讨了数据丰富对深度神经网络在超分辨率问题上的性能影响，并发现所有转换的混合提供了最佳结果。

数据增强中图像变换对深度神经网络性能的影响

Effects of Data Enrichment with Image Transformations on the Performance  of Deep Networks

Fine-grained classification is challenging because categories can only be
discriminated by subtle and local differences. Variances in the pose, scale or
rotation usually make the problem more difficult. Most fine-grained
classification systems follow the pipeline of finding foreground object or
object parts (where) to extract discriminative features (what).
In this paper, we propose to apply visual attention to fine-grained
classification task using deep neural network. Our pipeline integrates three
types of attention: the bottom-up attention that propose candidate patches, the
object-level top-down attention that selects relevant patches to a certain
object, and the part-level top-down attention that localizes discriminative
parts. We combine these attentions to train domain-specific deep nets, then use
it to improve both the what and where aspects. Importantly, we avoid using
expensive annotations like bounding box or part information from end-to-end.
The weak supervision constraint makes our work easier to generalize.
We have verified the effectiveness of the method on the subsets of ILSVRC2012
dataset and CUB200_2011 dataset. Our pipeline delivered significant
improvements and achieved the best accuracy under the weakest supervision
condition. The performance is competitive against other methods that rely on
additional annotations.

本文提出了一种使用深度神经网络应用视觉注意力于细粒度分类任务的新管道，通过整合三种类型的注意力，训练领域特定的深度网络，在弱监督约束下避免使用昂贵的注释，并在 ILSVRC2012 数据集和 CUB200_2011 数据集的子集上验证了该方法的有效性，取得了与其他方法相竞争的表现。