Image classification methods are usually trained to perform predictions taking into account a predefined group of known classes. Real-world problems, however, may not allow for a full knowledge of the input and label spaces, making failures in recognition a hazard to deep visual learning. Open set recognition methods are characterized by the ability to correctly identifying inputs of known and unknown classes. In this context, we propose GeMOS: simple and plug-and-play open set recognition modules that can be attached to pretrained Deep Neural Networks for visual recognition. The GeMOS framework pairs pre-trained Convolutional Neural Networks with generative models for open set recognition to extract open set scores for each sample, allowing for failure recognition in object recognition tasks. We conduct a thorough evaluation of the proposed method in comparison with state-of-the-art open set algorithms, finding that GeMOS either outperforms or is statistically indistinguishable from more complex and costly models.
翻译:通常对图像分类方法进行培训,以进行预测,同时考虑到预先界定的已知类别组。然而,现实世界问题可能无法使人们充分了解输入和标签空间,使识别失败对深视学习造成危害。开放集成识别方法的特点是能够正确识别已知和未知类别的投入。在这方面,我们提议GEMOS:简单和插插和播放开放的识别模块,可附于预先训练的深神经网络,以视觉识别。GEMOS框架将预先训练的革命神经网络配对成基因化模型,以开放设定识别为每个样本提取开放的分数,允许在对象识别任务中识别失败。我们彻底评估了拟议方法,与最新、最先进的开放集算法进行比较,发现GEMOS要么超越了或从统计上看无法区分更复杂和成本更高的模型。