论文标题
OOD增强可能与开放式识别相矛盾
OOD Augmentation May Be at Odds with Open-Set Recognition
论文作者
论文摘要
尽管图像分类方法取得了进步，但检测不属于培训类的样本仍然是一个挑战性的问题。最近，对这个主题引起了人们的兴趣，这被称为开放式识别（OSR）。在OSR中，目标是同时实现分类和检测到分布（OOD）样本。已经提出了一些想法，以通过复杂的技术进一步推动经验结果。我们认为，这种复杂性确实不是必需的。为此，我们已经表明，作为OSR的最简单基线，最大的软可能性（MSP）应用于视觉变形金刚（VIT）作为基本分类器，该基础分类器经过非EOOD增强训练可以超过许多最近的方法。非OOD扩大是不会将数据分布太多改变的数据。我们的结果表现优于CIFAR-10数据集中的最先进，并且比SVHN和MNIST中的大多数当前方法都更好。我们表明，培训增强对OSR任务中VIT的性能有重大影响，尽管它们应在增强样品中产生显着的多样性，但生成的样品OOD-NENS必须保持限制。
Despite advances in image classification methods, detecting the samples not belonging to the training classes is still a challenging problem. There has been a burst of interest in this subject recently, which is called Open-Set Recognition (OSR). In OSR, the goal is to achieve both the classification and detecting out-of-distribution (OOD) samples. Several ideas have been proposed to push the empirical result further through complicated techniques. We believe that such complication is indeed not necessary. To this end, we have shown that Maximum Softmax Probability (MSP), as the simplest baseline for OSR, applied on Vision Transformers (ViTs) as the base classifier that is trained with non-OOD augmentations can surprisingly outperform many recent methods. Non-OOD augmentations are the ones that do not alter the data distribution by much. Our results outperform state-of-the-art in CIFAR-10 datasets, and is also better than most of the current methods in SVHN and MNIST. We show that training augmentation has a significant effect on the performance of ViTs in the OSR tasks, and while they should produce significant diversity in the augmented samples, the generated sample OOD-ness must remain limited.
