In order to devise an anomaly detection model using only normal training
data, an autoencoder (AE) is typically trained to reconstruct the data. As a
result, the AE can extract normal representations in its latent space. During
test time, since AE is not trained using real anomalies, it is expected to
poorly reconstruct the anomalous data. However, several researchers have
observed that it is not the case. In this work, we propose to limit the
reconstruction capability of AE by introducing a novel latent constriction
loss, which is added to the existing reconstruction loss. By using our method,
no extra computational cost is added to the AE during test time. Evaluations
using three video anomaly detection benchmark datasets, i.e., Ped2, Avenue, and
ShanghaiTech, demonstrate the effectiveness of our method in limiting the
reconstruction capability of AE, which leads to a better anomaly detection
model.

通过引入一种新的潜变量约束损失来限制自动编码器的重构能力，本研究在不增加额外计算成本的情况下，提出了一种有效的限制异常重构能力的自动编码器的方法，从而提高异常检测模型的性能。

用仅包含正常训练数据的正常潜空间约束进行异常检测

Constricting Normal Latent Space for Anomaly Detection with Normal-only  Training Data

Industrial anomaly detection is an important task within computer vision with
a wide range of practical use cases. The small size of anomalous regions in
many real-world datasets necessitates processing the images at a high
resolution. This frequently poses significant challenges concerning memory
consumption during the model training and inference stages, leaving some
existing methods impractical for widespread adoption. To overcome this
challenge, we present the tiled ensemble approach, which reduces memory
consumption by dividing the input images into a grid of tiles and training a
dedicated model for each tile location. The tiled ensemble is compatible with
any existing anomaly detection model without the need for any modification of
the underlying architecture. By introducing overlapping tiles, we utilize the
benefits of traditional stacking ensembles, leading to further improvements in
anomaly detection capabilities beyond high resolution alone. We perform a
comprehensive analysis using diverse underlying architectures, including Padim,
PatchCore, FastFlow, and Reverse Distillation, on two standard anomaly
detection datasets: MVTec and VisA. Our method demonstrates a notable
improvement across setups while remaining within GPU memory constraints,
consuming only as much GPU memory as a single model needs to process a single
tile.

工业异常检测是计算机视觉中的重要任务，其实际用途广泛。为了解决处理高分辨率图像时出现的内存消耗问题，本文引入了块状集合的方法，在不修改底层架构的情况下，将输入图像划分为网格状瓦片，并为每个位置训练一个专用模型。通过引入重叠瓦片，利用传统的堆叠集合的优势，进一步提高了异常检测能力。实验证明，我们的方法在满足 GPU 内存限制的情况下，在 MVTec 和 VisA 两个标准异常检测数据集上取得了显著的改进。

分而治之：通过内存高效瓦片集成进行高分辨率工业异常检测

Divide and Conquer: High-Resolution Industrial Anomaly Detection via  Memory Efficient Tiled Ensemble

System logs are some of the most important information for the maintenance of
software systems, which have become larger and more complex in recent years.
The goal of log-based anomaly detection is to automatically detect system
anomalies by analyzing the large number of logs generated in a short period of
time, which is a critical challenge in the real world. Previous studies have
used a log parser to extract templates from unstructured log data and detect
anomalies on the basis of patterns of the template occurrences. These methods
have limitations for logs with unknown templates. Furthermore, since most log
anomalies are known to be point anomalies rather than contextual anomalies,
detection methods based on occurrence patterns can cause unnecessary delays in
detection. In this paper, we propose LogELECTRA, a new log anomaly detection
model that analyzes a single line of log messages more deeply on the basis of
self-supervised anomaly detection. LogELECTRA specializes in detecting log
anomalies as point anomalies by applying ELECTRA, a natural language processing
model, to analyze the semantics of a single line of log messages. LogELECTRA
outperformed existing state-of-the-art methods in experiments on the public
benchmark log datasets BGL, Sprit, and Thunderbird.

我们提出了一种新的日志异常检测模型 LogELECTRA，通过应用 ELECTRA 自然语言处理模型来分析单行日志消息的语义，LogELECTRA 专门用于检测点异常的日志异常，并在公共基准日志数据集 BGL、Sprit 和 Thunderbird 的实验中优于现有的最先进方法。