Detecting anomalies in temporal data is challenging due to anomalies being
dependent on temporal dynamics. One-class classification methods are commonly
used for anomaly detection tasks, but they have limitations when applied to
temporal data. In particular, mapping all normal instances into a single
hypersphere to capture their global characteristics can lead to poor
performance in detecting context-based anomalies where the abnormality is
defined with respect to local information. To address this limitation, we
propose a novel approach inspired by the loss function of DeepSVDD. Instead of
mapping all normal instances into a single hypersphere center, each normal
instance is pulled toward a recent context window. However, this approach is
prone to a representation collapse issue where the neural network that encodes
a given instance and its context is optimized towards a constant encoder
solution. To overcome this problem, we combine our approach with a
deterministic contrastive loss from Neutral AD, a promising self-supervised
learning anomaly detection approach. We provide a theoretical analysis to
demonstrate that the incorporation of the deterministic contrastive loss can
effectively prevent the occurrence of a constant encoder solution. Experimental
results show superior performance of our model over various baselines and model
variants on real-world industrial datasets.

针对时间数据中的异常检测问题，提出了一种新的方法，借鉴了 DeepSVDD 的损失函数，结合了 Neutral AD 的确定性对比损失，提高了在实际工业数据集中的性能表现。

基於上下文區分對比學習的時間序列異常檢測

Time-series Anomaly Detection via Contextual Discriminative Contrastive  Learning

Anomaly detection methods require high-quality features. In recent years, the
anomaly detection community has attempted to obtain better features using
advances in deep self-supervised feature learning. Surprisingly, a very
promising direction, using pretrained deep features, has been mostly
overlooked. In this paper, we first empirically establish the perhaps expected,
but unreported result, that combining pretrained features with simple anomaly
detection and segmentation methods convincingly outperforms, much more complex,
state-of-the-art methods.
In order to obtain further performance gains in anomaly detection, we adapt
pretrained features to the target distribution. Although transfer learning
methods are well established in multi-class classification problems, the
one-class classification (OCC) setting is not as well explored. It turns out
that naive adaptation methods, which typically work well in supervised
learning, often result in catastrophic collapse (feature deterioration) and
reduce performance in OCC settings. A popular OCC method, DeepSVDD, advocates
using specialized architectures, but this limits the adaptation performance
gain. We propose two methods for combating collapse: i) a variant of early
stopping that dynamically learns the stopping iteration ii) elastic
regularization inspired by continual learning. Our method, PANDA, outperforms
the state-of-the-art in the OCC, outlier exposure and anomaly segmentation
settings by large margins.

本文研究在异常检测领域中采用预训练特征的方法，并通过对特征的动态适应以及弹性正则化等方式提高异常检测、异常暴露和异常分割的性能，取得了领先于现有方法的效果。