61,481 research outputs found
FractalAD: A simple industrial anomaly detection method using fractal anomaly generation and backbone knowledge distillation
Although industrial anomaly detection (AD) technology has made significant
progress in recent years, generating realistic anomalies and learning priors of
normal remain challenging tasks. In this study, we propose an end-to-end
industrial anomaly detection method called FractalAD. Training samples are
obtained by synthesizing fractal images and patches from normal samples. This
fractal anomaly generation method is designed to sample the full morphology of
anomalies. Moreover, we designed a backbone knowledge distillation structure to
extract prior knowledge contained in normal samples. The differences between a
teacher and a student model are converted into anomaly attention using a cosine
similarity attention module. The proposed method enables an end-to-end semantic
segmentation network to be used for anomaly detection without adding any
trainable parameters to the backbone and segmentation head, and has obvious
advantages over other methods in training and inference speed.. The results of
ablation studies confirmed the effectiveness of fractal anomaly generation and
backbone knowledge distillation. The results of performance experiments showed
that FractalAD achieved competitive results on the MVTec AD dataset and MVTec
3D-AD dataset compared with other state-of-the-art anomaly detection methods.Comment: 12 pages, 5 figure
Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress
Time series anomaly detection has been a perennially important topic in data
science, with papers dating back to the 1950s. However, in recent years there
has been an explosion of interest in this topic, much of it driven by the
success of deep learning in other domains and for other time series tasks. Most
of these papers test on one or more of a handful of popular benchmark datasets,
created by Yahoo, Numenta, NASA, etc. In this work we make a surprising claim.
The majority of the individual exemplars in these datasets suffer from one or
more of four flaws. Because of these four flaws, we believe that many published
comparisons of anomaly detection algorithms may be unreliable, and more
importantly, much of the apparent progress in recent years may be illusionary.
In addition to demonstrating these claims, with this paper we introduce the UCR
Time Series Anomaly Archive. We believe that this resource will perform a
similar role as the UCR Time Series Classification Archive, by providing the
community with a benchmark that allows meaningful comparisons between
approaches and a meaningful gauge of overall progress
TeD-SPAD: Temporal Distinctiveness for Self-supervised Privacy-preservation for video Anomaly Detection
Video anomaly detection (VAD) without human monitoring is a complex computer
vision task that can have a positive impact on society if implemented
successfully. While recent advances have made significant progress in solving
this task, most existing approaches overlook a critical real-world concern:
privacy. With the increasing popularity of artificial intelligence
technologies, it becomes crucial to implement proper AI ethics into their
development. Privacy leakage in VAD allows models to pick up and amplify
unnecessary biases related to people's personal information, which may lead to
undesirable decision making. In this paper, we propose TeD-SPAD, a
privacy-aware video anomaly detection framework that destroys visual private
information in a self-supervised manner. In particular, we propose the use of a
temporally-distinct triplet loss to promote temporally discriminative features,
which complements current weakly-supervised VAD methods. Using TeD-SPAD, we
achieve a positive trade-off between privacy protection and utility anomaly
detection performance on three popular weakly supervised VAD datasets:
UCF-Crime, XD-Violence, and ShanghaiTech. Our proposed anonymization model
reduces private attribute prediction by 32.25% while only reducing frame-level
ROC AUC on the UCF-Crime anomaly detection dataset by 3.69%. Project Page:
https://joefioresi718.github.io/TeD-SPAD_webpage/Comment: ICCV 202
Graph-based Time-Series Anomaly Detection: A Survey
With the recent advances in technology, a wide range of systems continue to
collect a large amount of data over time and thus generate time series.
Time-Series Anomaly Detection (TSAD) is an important task in various
time-series applications such as e-commerce, cybersecurity, vehicle
maintenance, and healthcare monitoring. However, this task is very challenging
as it requires considering both the intra-variable dependency and the
inter-variable dependency, where a variable can be defined as an observation in
time series data. Recent graph-based approaches have made impressive progress
in tackling the challenges of this field. In this survey, we conduct a
comprehensive and up-to-date review of Graph-based TSAD (G-TSAD). First, we
explore the significant potential of graph representation learning for
time-series data. Then, we review state-of-the-art graph anomaly detection
techniques in the context of time series and discuss their strengths and
drawbacks. Finally, we discuss the technical challenges and potential future
directions for possible improvements in this research field.Comment: 19 pages, 4 figures, 2 table
Bridging Machine Learning and Sciences: Opportunities and Challenges
The application of machine learning in sciences has seen exciting advances in
recent years. As a widely applicable technique, anomaly detection has been long
studied in the machine learning community. Especially, deep neural nets-based
out-of-distribution detection has made great progress for high-dimensional
data. Recently, these techniques have been showing their potential in
scientific disciplines. We take a critical look at their applicative prospects
including data universality, experimental protocols, model robustness, etc. We
discuss examples that display transferable practices and domain-specific
challenges simultaneously, providing a starting point for establishing a novel
interdisciplinary research paradigm in the near future.Comment: 8 pages, 3 figure
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