24,324 research outputs found
Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIs
Unsupervised anomaly detection in Brain MRIs aims to identify abnormalities
as outliers from a healthy training distribution. Reconstruction-based
approaches that use generative models to learn to reconstruct healthy brain
anatomy are commonly used for this task. Diffusion models are an emerging class
of deep generative models that show great potential regarding reconstruction
fidelity. However, they face challenges in preserving intensity characteristics
in the reconstructed images, limiting their performance in anomaly detection.
To address this challenge, we propose to condition the denoising mechanism of
diffusion models with additional information about the image to reconstruct
coming from a latent representation of the noise-free input image. This
conditioning enables high-fidelity reconstruction of healthy brain structures
while aligning local intensity characteristics of input-reconstruction pairs.
We evaluate our method's reconstruction quality, domain adaptation features and
finally segmentation performance on publicly available data sets with various
pathologies. Using our proposed conditioning mechanism we can reduce the
false-positive predictions and enable a more precise delineation of anomalies
which significantly enhances the anomaly detection performance compared to
established state-of-the-art approaches to unsupervised anomaly detection in
brain MRI. Furthermore, our approach shows promise in domain adaptation across
different MRI acquisitions and simulated contrasts, a crucial property of
general anomaly detection methods.Comment: Preprin
Zero-Shot Anomaly Detection without Foundation Models
Anomaly detection (AD) tries to identify data instances that deviate from the
norm in a given data set. Since data distributions are subject to distribution
shifts, our concept of ``normality" may also drift, raising the need for
zero-shot adaptation approaches for anomaly detection. However, the fact that
current zero-shot AD methods rely on foundation models that are restricted in
their domain (natural language and natural images), are costly, and oftentimes
proprietary, asks for alternative approaches. In this paper, we propose a
simple and highly effective zero-shot AD approach compatible with a variety of
established AD methods. Our solution relies on training an off-the-shelf
anomaly detector (such as a deep SVDD) on a set of inter-related data
distributions in combination with batch normalization. This simple
recipe--batch normalization plus meta-training--is a highly effective and
versatile tool. Our results demonstrate the first zero-shot anomaly detection
results for tabular data and SOTA zero-shot AD results for image data from
specialized domains.Comment: anomaly detection, zero-shot learning, batch normalizatio
2nd Place Winning Solution for the CVPR2023 Visual Anomaly and Novelty Detection Challenge: Multimodal Prompting for Data-centric Anomaly Detection
This technical report introduces the winning solution of the team Segment Any
Anomaly for the CVPR2023 Visual Anomaly and Novelty Detection (VAND) challenge.
Going beyond uni-modal prompt, e.g., language prompt, we present a novel
framework, i.e., Segment Any Anomaly + (SAA), for zero-shot anomaly
segmentation with multi-modal prompts for the regularization of cascaded modern
foundation models. Inspired by the great zero-shot generalization ability of
foundation models like Segment Anything, we first explore their assembly (SAA)
to leverage diverse multi-modal prior knowledge for anomaly localization.
Subsequently, we further introduce multimodal prompts (SAA) derived from
domain expert knowledge and target image context to enable the non-parameter
adaptation of foundation models to anomaly segmentation. The proposed SAA
model achieves state-of-the-art performance on several anomaly segmentation
benchmarks, including VisA and MVTec-AD, in the zero-shot setting. We will
release the code of our winning solution for the CVPR2023 VAN.Comment: The first two author contribute equally. CVPR workshop challenge
report. arXiv admin note: substantial text overlap with arXiv:2305.1072
Tiresias: Online Anomaly Detection for Hierarchical Operational Network Data
Operational network data, management data such as customer care call logs and
equipment system logs, is a very important source of information for network
operators to detect problems in their networks. Unfortunately, there is lack of
efficient tools to automatically track and detect anomalous events on
operational data, causing ISP operators to rely on manual inspection of this
data. While anomaly detection has been widely studied in the context of network
data, operational data presents several new challenges, including the
volatility and sparseness of data, and the need to perform fast detection
(complicating application of schemes that require offline processing or
large/stable data sets to converge).
To address these challenges, we propose Tiresias, an automated approach to
locating anomalous events on hierarchical operational data. Tiresias leverages
the hierarchical structure of operational data to identify high-impact
aggregates (e.g., locations in the network, failure modes) likely to be
associated with anomalous events. To accommodate different kinds of operational
network data, Tiresias consists of an online detection algorithm with low time
and space complexity, while preserving high detection accuracy. We present
results from two case studies using operational data collected at a large
commercial IP network operated by a Tier-1 ISP: customer care call logs and
set-top box crash logs. By comparing with a reference set verified by the ISP's
operational group, we validate that Tiresias can achieve >94% accuracy in
locating anomalies. Tiresias also discovered several previously unknown
anomalies in the ISP's customer care cases, demonstrating its effectiveness
AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments
This report considers the application of Articial Intelligence (AI) techniques to
the problem of misuse detection and misuse localisation within telecommunications
environments. A broad survey of techniques is provided, that covers inter alia
rule based systems, model-based systems, case based reasoning, pattern matching,
clustering and feature extraction, articial neural networks, genetic algorithms, arti
cial immune systems, agent based systems, data mining and a variety of hybrid
approaches. The report then considers the central issue of event correlation, that
is at the heart of many misuse detection and localisation systems. The notion of
being able to infer misuse by the correlation of individual temporally distributed
events within a multiple data stream environment is explored, and a range of techniques,
covering model based approaches, `programmed' AI and machine learning
paradigms. It is found that, in general, correlation is best achieved via rule based approaches,
but that these suffer from a number of drawbacks, such as the difculty of
developing and maintaining an appropriate knowledge base, and the lack of ability
to generalise from known misuses to new unseen misuses. Two distinct approaches
are evident. One attempts to encode knowledge of known misuses, typically within
rules, and use this to screen events. This approach cannot generally detect misuses
for which it has not been programmed, i.e. it is prone to issuing false negatives.
The other attempts to `learn' the features of event patterns that constitute normal
behaviour, and, by observing patterns that do not match expected behaviour, detect
when a misuse has occurred. This approach is prone to issuing false positives,
i.e. inferring misuse from innocent patterns of behaviour that the system was not
trained to recognise. Contemporary approaches are seen to favour hybridisation,
often combining detection or localisation mechanisms for both abnormal and normal
behaviour, the former to capture known cases of misuse, the latter to capture
unknown cases. In some systems, these mechanisms even work together to update
each other to increase detection rates and lower false positive rates. It is concluded
that hybridisation offers the most promising future direction, but that a rule or state
based component is likely to remain, being the most natural approach to the correlation
of complex events. The challenge, then, is to mitigate the weaknesses of
canonical programmed systems such that learning, generalisation and adaptation
are more readily facilitated
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