论文标题
使用数据增强，焦点余弦丢失和集合的数据有效的深度学习方法用于图像分类
Data-Efficient Deep Learning Method for Image Classification Using Data Augmentation, Focal Cosine Loss, and Ensemble
论文作者
论文摘要
通常，足够的数据对于深入学习模型的更好性能和概括至关重要。但是，数据收集的许多局限性（成本，资源等）导致大多数领域缺乏足够的数据。此外，每个数据源和许可证的各个领域也导致收集足够数据的困难。这种情况使我们不仅很难利用预训练的模型，而且还难以利用外部知识。因此，重要的是要有效地利用小型数据集来实现更好的性能。我们在三个方面应用了一些技术：数据，损失函数和预测以使较少的数据从头开始培训。使用这些方法，我们通过利用ImageNet数据来获得高精度，该数据仅由每类仅50张图像组成。此外，对于数据有效的计算机视觉挑战，我们的模型在视觉归纳打印机中排名第四。
In general, sufficient data is essential for the better performance and generalization of deep-learning models. However, lots of limitations(cost, resources, etc.) of data collection leads to lack of enough data in most of the areas. In addition, various domains of each data sources and licenses also lead to difficulties in collection of sufficient data. This situation makes us hard to utilize not only the pre-trained model, but also the external knowledge. Therefore, it is important to leverage small dataset effectively for achieving the better performance. We applied some techniques in three aspects: data, loss function, and prediction to enable training from scratch with less data. With these methods, we obtain high accuracy by leveraging ImageNet data which consist of only 50 images per class. Furthermore, our model is ranked 4th in Visual Inductive Printers for Data-Effective Computer Vision Challenge.
