论文标题
Env-Aware Anomaly检测：忽略样式变化，忠于内容！
Env-Aware Anomaly Detection: Ignore Style Changes, Stay True to Content!
论文作者
论文摘要
我们为在分配转移方案中无监督的异常检测任务引入了形式化和基准。我们的工作基于IWildCAM数据集，据我们所知，我们是第一个提出这种视觉数据方法的人。我们从经验上验证了与基本的经验风险最小化（ERM）相比，在这种情况下，环境感知方法的性能更好。接下来，我们提出了一个扩展，用于生成阳性样品的对比方法，该方法在训练时考虑了环境标签，将ERM基线得分提高了8.7％。
We introduce a formalization and benchmark for the unsupervised anomaly detection task in the distribution-shift scenario. Our work builds upon the iWildCam dataset, and, to the best of our knowledge, we are the first to propose such an approach for visual data. We empirically validate that environment-aware methods perform better in such cases when compared with the basic Empirical Risk Minimization (ERM). We next propose an extension for generating positive samples for contrastive methods that considers the environment labels when training, improving the ERM baseline score by 8.7%.
