15,307 research outputs found
Weakly supervised marine animal detection from remote sensing images using vector-quantized variational autoencoder
This paper studies a reconstruction-based approach for weakly-supervised
animal detection from aerial images in marine environments. Such an approach
leverages an anomaly detection framework that computes metrics directly on the
input space, enhancing interpretability and anomaly localization compared to
feature embedding methods. Building upon the success of Vector-Quantized
Variational Autoencoders in anomaly detection on computer vision datasets, we
adapt them to the marine animal detection domain and address the challenge of
handling noisy data. To evaluate our approach, we compare it with existing
methods in the context of marine animal detection from aerial image data.
Experiments conducted on two dedicated datasets demonstrate the superior
performance of the proposed method over recent studies in the literature. Our
framework offers improved interpretability and localization of anomalies,
providing valuable insights for monitoring marine ecosystems and mitigating the
impact of human activities on marine animals.Comment: 4 pages, accepted to IGARSS 202
A Supervised Embedding and Clustering Anomaly Detection method for classification of Mobile Network Faults
The paper introduces Supervised Embedding and Clustering Anomaly Detection
(SEMC-AD), a method designed to efficiently identify faulty alarm logs in a
mobile network and alleviate the challenges of manual monitoring caused by the
growing volume of alarm logs. SEMC-AD employs a supervised embedding approach
based on deep neural networks, utilizing historical alarm logs and their labels
to extract numerical representations for each log, effectively addressing the
issue of imbalanced classification due to a small proportion of anomalies in
the dataset without employing one-hot encoding. The robustness of the embedding
is evaluated by plotting the two most significant principle components of the
embedded alarm logs, revealing that anomalies form distinct clusters with
similar embeddings. Multivariate normal Gaussian clustering is then applied to
these components, identifying clusters with a high ratio of anomalies to normal
alarms (above 90%) and labeling them as the anomaly group. To classify new
alarm logs, we check if their embedded vectors' two most significant principle
components fall within the anomaly-labeled clusters. If so, the log is
classified as an anomaly. Performance evaluation demonstrates that SEMC-AD
outperforms conventional random forest and gradient boosting methods without
embedding. SEMC-AD achieves 99% anomaly detection, whereas random forest and
XGBoost only detect 86% and 81% of anomalies, respectively. While supervised
classification methods may excel in labeled datasets, the results demonstrate
that SEMC-AD is more efficient in classifying anomalies in datasets with
numerous categorical features, significantly enhancing anomaly detection,
reducing operator burden, and improving network maintenance
Integrating State-of-the-Art Approaches for Anomaly Detection and Localization in the Continual Learning Setting
openThe significant attention surrounding the application of anomaly detection (AD) in identifying defects within industrial environments using only normal samples has prompted research and development in this area. However, traditional AD methods have been primarily focused on the current set of examples, resulting in a limitation known as catastrophic forgetting when encountering new tasks. The inflexibility of these methods and the challenges posed by real-world industrial scenarios necessitate the urgent enhancement of the adaptive capabilities of AD models. Therefore, this thesis presents an integrated framework that combines the concepts of continual learning (CL) and anomaly detection (AD) to achieve the objective of anomaly detection in continual learning (ADCL). To evaluate the efficacy of the framework, a thorough comparative analysis is conducted to assess the performance of three specific methods for the AD task: the EfficientAD, Patch Distribution Modeling Framework (PaDiM) and the Discriminatively Trained Reconstruction Anomaly Embedding Model (DRAEM). Moreover, the framework incorporates the use of replay techniques to enable continual learning (CL). In order to determine the superior technique, a comprehensive evaluation is carried out using diverse metrics that measure the relative performance of each method. To validate the proposed approach, a robust real-world dataset called MVTec AD is employed, consisting of images with pixel-based anomalies. This dataset serves as a reliable benchmark for Anomaly Detection in the context of Continual Learning, offering a solid foundation for further advancements in this field of study.The significant attention surrounding the application of anomaly detection (AD) in identifying defects within industrial environments using only normal samples has prompted research and development in this area. However, traditional AD methods have been primarily focused on the current set of examples, resulting in a limitation known as catastrophic forgetting when encountering new tasks. The inflexibility of these methods and the challenges posed by real-world industrial scenarios necessitate the urgent enhancement of the adaptive capabilities of AD models. Therefore, this thesis presents an integrated framework that combines the concepts of continual learning (CL) and anomaly detection (AD) to achieve the objective of anomaly detection in continual learning (ADCL). To evaluate the efficacy of the framework, a thorough comparative analysis is conducted to assess the performance of three specific methods for the AD task: the EfficientAD, Patch Distribution Modeling Framework (PaDiM) and the Discriminatively Trained Reconstruction Anomaly Embedding Model (DRAEM). Moreover, the framework incorporates the use of replay techniques to enable continual learning (CL). In order to determine the superior technique, a comprehensive evaluation is carried out using diverse metrics that measure the relative performance of each method. To validate the proposed approach, a robust real-world dataset called MVTec AD is employed, consisting of images with pixel-based anomalies. This dataset serves as a reliable benchmark for Anomaly Detection in the context of Continual Learning, offering a solid foundation for further advancements in this field of study
FRE: A Fast Method For Anomaly Detection And Segmentation
This paper presents a fast and principled approach for solving the visual
anomaly detection and segmentation problem. In this setup, we have access to
only anomaly-free training data and want to detect and identify anomalies of an
arbitrary nature on test data. We propose the application of linear statistical
dimensionality reduction techniques on the intermediate features produced by a
pretrained DNN on the training data, in order to capture the low-dimensional
subspace truly spanned by said features. We show that the \emph{feature
reconstruction error} (FRE), which is the -norm of the difference
between the original feature in the high-dimensional space and the pre-image of
its low-dimensional reduced embedding, is extremely effective for anomaly
detection. Further, using the same feature reconstruction error concept on
intermediate convolutional layers, we derive FRE maps that provide pixel-level
spatial localization of the anomalies in the image (i.e. segmentation).
Experiments using standard anomaly detection datasets and DNN architectures
demonstrate that our method matches or exceeds best-in-class quality
performance, but at a fraction of the computational and memory cost required by
the state of the art. It can be trained and run very efficiently, even on a
traditional CPU.Comment: arXiv admin note: text overlap with arXiv:2203.1042
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