论文标题
可靠的OOD图像分类的多层表示学习
Multi-layer Representation Learning for Robust OOD Image Classification
论文作者
论文摘要
卷积神经网络已成为图像分类的规范。然而，在过去的几年中，它们在整个数据集中保持高精度的困难已变得显而易见。为了在实际情况和应用程序中利用此类模型，它们必须能够对看不见的数据提供值得信赖的预测。在本文中，我们认为从CNN的中间层中提取功能可以帮助模型的最终预测。具体而言，我们将HyperColumns方法适应RESNET-18，并在评估NICO数据集时发现模型的准确性显着提高。
Convolutional Neural Networks have become the norm in image classification. Nevertheless, their difficulty to maintain high accuracy across datasets has become apparent in the past few years. In order to utilize such models in real-world scenarios and applications, they must be able to provide trustworthy predictions on unseen data. In this paper, we argue that extracting features from a CNN's intermediate layers can assist in the model's final prediction. Specifically, we adapt the Hypercolumns method to a ResNet-18 and find a significant increase in the model's accuracy, when evaluating on the NICO dataset.
