A new Lossy Causal Temporal Convolutional Neural Network Autoencoder for
anomaly detection is proposed in this work. Our framework uses a
rate-distortion loss and an entropy bottleneck to learn a compressed latent
representation for the task. The main idea of using a rate-distortion loss is
to introduce representation flexibility that ignores or becomes robust to
unlikely events with distinctive patterns, such as anomalies. These anomalies
manifest as unique distortion features that can be accurately detected in
testing conditions. This new architecture allows us to train a fully
unsupervised model that has high accuracy in detecting anomalies from a
distortion score despite being trained with some portion of unlabelled
anomalous data. This setting is in stark contrast to many of the
state-of-the-art unsupervised methodologies that require the model to be only
trained on "normal data". We argue that this partially violates the concept of
unsupervised training for anomaly detection as the model uses an informed
decision that selects what is normal from abnormal for training. Additionally,
there is evidence to suggest it also effects the models ability at
generalisation. We demonstrate that models that succeed in the paradigm where
they are only trained on normal data fail to be robust when anomalous data is
injected into the training. In contrast, our compression-based approach
converges to a robust representation that tolerates some anomalous distortion.
The robust representation achieved by a model using a rate-distortion loss can
be used in a more realistic unsupervised anomaly detection scheme.

本文提出了一种新的损失因果时间卷积神经网络自编码器来进行异常检测，其使用速率 - 失真损失和熵瓶颈来学习压缩编码的潜在表示，有助于实现更实际的无监督异常检测方案。

面向鲁棒性无监督时间序列异常检测的有损压缩

Lossy Compression for Robust Unsupervised Time-Series Anomaly Detection

In this paper we present a a deep generative model for lossy video
compression. We employ a model that consists of a 3D autoencoder with a
discrete latent space and an autoregressive prior used for entropy coding. Both
autoencoder and prior are trained jointly to minimize a rate-distortion loss,
which is closely related to the ELBO used in variational autoencoders. Despite
its simplicity, we find that our method outperforms the state-of-the-art
learned video compression networks based on motion compensation or
interpolation. We systematically evaluate various design choices, such as the
use of frame-based or spatio-temporal autoencoders, and the type of
autoregressive prior. In addition, we present three extensions of the basic
method that demonstrate the benefits over classical approaches to compression.
First, we introduce semantic compression, where the model is trained to
allocate more bits to objects of interest. Second, we study adaptive
compression, where the model is adapted to a domain with limited variability,
e.g., videos taken from an autonomous car, to achieve superior compression on
that domain. Finally, we introduce multimodal compression, where we demonstrate
the effectiveness of our model in joint compression of multiple modalities
captured by non-standard imaging sensors, such as quad cameras. We believe that
this opens up novel video compression applications, which have not been
feasible with classical codecs.

本文提出了一种基于深度生成模型的视频压缩方法，采用三维自编码器和自回归先验以最小化速率失真损失，实现编码效果优于现有的基于运动补偿或插值的视频压缩网络，并且在设计选择和压缩技术方面进行了系统评估和扩展，包括语义压缩、自适应压缩和多模态压缩，以便在非标准成像传感器捕获的多个模态数据上实现联合压缩。