Download Unknown Proxies Anom
In general, an important capability of AI systems is to identify the unknown. However, when striving for improved self-reflection capabilities, anomaly detection is not sufficient. Another important capability for real-world deployment of AI systems is to realize that some specific concept appears over and over again and potentially constitutes a new (or novel) object class. Incremental learning refers to the task of learning new classes, however, especially in semantic segmentation, mostly in a strictly supervised or semi-supervised fashion where data for the new class is labeled with ground truth [MZ19, CMB+20]. This is accompanied by an enormous data collection and annotation effort. In contrast to supervised incremental learning, humans may recognize a novelty of a given class that appears over and over again very well, such that in the end a single feedback might be sufficient to assign a name to a novel class. For the task of image classification, [HZ21] provides an unsupervised extension of the semantic space, while for segmentation there exist only approaches for supervised extension of the semantic space via incremental learning.
Download Unknown Proxies anom
In this chapter, we first introduce anomaly detection from an information-based perspective in Sect. 2. We provide theoretical evidence that the entropy is a suitable quantity for anomaly detection, particularly in semantic segmentation. In Sect. 3, we review recent developments in the fields of anomaly detection and unsupervised learning of new classes. We give an overview on existing methods, both in the context of image classification and semantic segmentation. In this setting, we present an approach to train semantic segmentation DNNs for high entropy on anomaly data in Sect. 4. We compare our proposed approach against other established and recent state-of-the-art anomaly segmentation methods and empirically show the effectiveness of entropy maximization in identifying unknown objects. Lastly, we propose an unsupervised learning technique for novel object classes in Sect. 5. Further, we provide an outlook how the latter approach can be combined with entropy maximization to handle the unknown at run time in automated driving.
After the introduction to anomaly detection from a theoretical point of view, we now turn to anomaly detection in deep learning. In this section, we review research in the direction of detecting and learning unknown objects in semantic segmentation.
Another recent line of works performs anomaly segmentation via generative models that reconstruct original input images. These methods assume that reconstructed images will better preserve the appearance of known image regions than that of unknown ones. Anomalous regions are then identified by means of pixel-wise discrepancies between the original and reconstructed image. Thus, such an approach is specifically designed to anomaly segmentation and has been extensively studied in [CM15, MVD17, LNFS19, XZL+20, LHFS21, BBSC21]. The main benefit of these approaches is that they do not require any OoD training data, allowing them to generalize to all possible anomalous objects. However, all these methods are limited by the integrated discrepancy module, i.e., the module that identifies relevant differences between the original and reconstructed image. In complex scenes, such as street scene images for automated driving, this might be a challenging task due to the open world setting.
Each of the methods employed in this section provides such score maps. Their underlying segmentation networks (DeepLabV3+, [CZP+18]) are all trained on Cityscapes [COR+16], i.e., objects not included in the set of Cityscapes object classes are considered as anomalies since they have not been seen during training and thus are unknown. The anomaly detection methods, however, differ in the way how the scores are obtained, which is why we briefly introduce the different techniques in the following.
where \(\mathcal D\) denotes non-anomaly training data (labels available) and \(\mathcal D^\mathrm anom\) denotes anomaly training data (no labels available). In this approach, the COCO dataset [LMB+14] represents a set of so-called known unknowns, which is used as proxy for \(\mathcal D^\mathrm anom\) with the aim to represent all possible anomaly data. Moreover, \(\lambda \in \mathbb I\) is a hyperparameter controlling the impact of each single loss function on the overall loss \(J^\mathrm total\). For non-anomaly data, the loss function is chosen to be the commonly-used cross-entropy \(J^\mathrm CE\), while for anomaly data, i.e., for known unknowns, we have
In general, we observe that anomaly detection methods originally designed for image classification, including MSP, ODIN and Mahalanobis, do not generalize well to anomaly segmentation. As the Mahalanobis distance is based on statistics of the Cityscapes dataset, the anomaly detection is likely to suffer from performance loss under domain shift. The same holds for Monte Carlo dropout and learned embedding density, particularly resulting in poor performance in RoadObstacle21, where various road surfaces are available. Therefore, those methods potentially act as domain shift classifier rather than as detector of unknown objects.
The idea of meta classification can even be used to directly identify potential anomalous objects in the semantic segmentation mask, see [ORG18], which will be subject to discussion in the following section about unsupervised learning of unknown objects.
For anomaly segmentation, we considered a number of generic baseline methods stemming from image classification as well as some recent anomaly segmentation methods. Since the latter clearly outperforms the former, this stresses the need for the development of methods specifically designed for anomaly segmentation. We have demonstrated with our entropy maximization method empirically as well as theoretically that good proxies in combination with training on anomaly examples for high entropy are key to equip standard semantic segmentation models with anomaly segmentation capabilities. Particularly on the challenging RoadObstacle21 dataset with diverse street scenarios, entropy maximization yields great performance which is not reached by any other method so far. While there exists a moderate number of datasets for anomaly segmentation, there is clearly still the need of additional datasets. The number of possible unknown object classes not covered by these datasets is evidently enormous. Furthermore, also the vast variety of possible environmental conditions and further domain shifts that may occur, possibly also in combination with unknown objects, continuously demand their exploration.
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In this simple model, N0 and the 3 constants a, b, and c are unknown. Their values can be obtained by the means of a least square iterative routine providing the best possible fit for Equation (2) to the actual data derived from ERA5. This adjustment can be done for the 5 widths of averaging windows already considered previously. In each case, the quality of the adjustment is denoted by R2, and by the slope and vertical intercept of the plot of the modelled Nanom against the observed ones. Finally, the interest of including or not, the corrective terms corresponding to the PDO and/or ENSO effects can be evidenced by forcing either b or c (no PDO or no ENSO effect), or both of them (no PDO and no ENSO effect) to be 0. The results of these experiments are summarized in Table 2. 041b061a72