论文标题
使用深度估计作为一项辅助任务，将噪声和有限的数据集大小降至图像分类中的效果，并具有深度的多任务学习
Minimizing the Effect of Noise and Limited Dataset Size in Image Classification Using Depth Estimation as an Auxiliary Task with Deep Multitask Learning
论文作者
论文摘要
概括性是机器学习（ML）图像分类器的最终目标，其中噪声和有限的数据集大小是主要问题。我们通过利用深度多任务学习（DMTL）的框架来应对这些挑战，并将图像深度估计作为一项辅助任务。在MNIST数据集的自定义和深度增强的推导下，我们表明a）多任务损耗功能是实施DMTL的最有效方法，b）有限的数据集大小主要导致分类不准确，并且c）深度估计主要受噪声的影响。为了进一步验证结果，我们手动将NYU深度V2数据集标记为场景分类任务。作为对该领域的贡献，我们以Python Antial Gormat作为开源数据集提供了数据，并提供了场景标签。我们对MNIST和NYU-DEPTH-V2的实验表明，当数据集嘈杂并且示例的数量受到限制时，DMTL可提高分类器的普遍性。
Generalizability is the ultimate goal of Machine Learning (ML) image classifiers, for which noise and limited dataset size are among the major concerns. We tackle these challenges through utilizing the framework of deep Multitask Learning (dMTL) and incorporating image depth estimation as an auxiliary task. On a customized and depth-augmented derivation of the MNIST dataset, we show a) multitask loss functions are the most effective approach of implementing dMTL, b) limited dataset size primarily contributes to classification inaccuracy, and c) depth estimation is mostly impacted by noise. In order to further validate the results, we manually labeled the NYU Depth V2 dataset for scene classification tasks. As a contribution to the field, we have made the data in python native format publicly available as an open-source dataset and provided the scene labels. Our experiments on MNIST and NYU-Depth-V2 show dMTL improves generalizability of the classifiers when the dataset is noisy and the number of examples is limited.
