论文标题
图像分类中数据排序的影响
The Effect of Data Ordering in Image Classification
论文作者
论文摘要
深度学习模型的成功故事每天都会增加从图像分类到自然语言理解的不同任务。随着这些模型的日益普及，科学家花费越来越多的时间为其任务找到最佳的参数和最佳模型架构。在本文中，我们专注于为这些机器提供的成分：数据。我们假设数据排序会影响模型的性能。为此，我们使用ImageNet数据集对图像分类任务进行实验，并证明某些数据顺序在获得更高的分类精度方面比其他数据订购更好。实验结果表明，与模型结构，学习率和批处理大小无关，数据的排序显着影响结果。我们使用不同的指标来显示这些发现：NDCG，准确性 @ 1和准确性 @ 5。我们的目标是证明不仅参数和模型架构，而且数据订购也有发言权，可以获得更好的结果。
The success stories from deep learning models increase every day spanning different tasks from image classification to natural language understanding. With the increasing popularity of these models, scientists spend more and more time finding the optimal parameters and best model architectures for their tasks. In this paper, we focus on the ingredient that feeds these machines: the data. We hypothesize that the data ordering affects how well a model performs. To that end, we conduct experiments on an image classification task using ImageNet dataset and show that some data orderings are better than others in terms of obtaining higher classification accuracies. Experimental results show that independent of model architecture, learning rate and batch size, ordering of the data significantly affects the outcome. We show these findings using different metrics: NDCG, accuracy @ 1 and accuracy @ 5. Our goal here is to show that not only parameters and model architectures but also the data ordering has a say in obtaining better results.
