677 research outputs found
A Comprehensive Augmentation Framework for Anomaly Detection
Data augmentation methods are commonly integrated into the training of
anomaly detection models. Previous approaches have primarily focused on
replicating real-world anomalies or enhancing diversity, without considering
that the standard of anomaly varies across different classes, potentially
leading to a biased training distribution.This paper analyzes crucial traits of
simulated anomalies that contribute to the training of reconstructive networks
and condenses them into several methods, thus creating a comprehensive
framework by selectively utilizing appropriate combinations.Furthermore, we
integrate this framework with a reconstruction-based approach and concurrently
propose a split training strategy that alleviates the issue of overfitting
while avoiding introducing interference to the reconstruction process. The
evaluations conducted on the MVTec anomaly detection dataset demonstrate that
our method outperforms the previous state-of-the-art approach, particularly in
terms of object classes. To evaluate generalizability, we generate a simulated
dataset comprising anomalies with diverse characteristics since the original
test samples only include specific types of anomalies and may lead to biased
evaluations. Experimental results demonstrate that our approach exhibits
promising potential for generalizing effectively to various unforeseen
anomalies encountered in real-world scenarios
Exploring the Relationship between Samples and Masks for Robust Defect Localization
Defect detection aims to detect and localize regions out of the normal
distribution.Previous approaches model normality and compare it with the input
to identify defective regions, potentially limiting their generalizability.This
paper proposes a one-stage framework that detects defective patterns directly
without the modeling process.This ability is adopted through the joint efforts
of three parties: a generative adversarial network (GAN), a newly proposed
scaled pattern loss, and a dynamic masked cycle-consistent auxiliary network.
Explicit information that could indicate the position of defects is
intentionally excluded to avoid learning any direct mapping.Experimental
results on the texture class of the challenging MVTec AD dataset show that the
proposed method is 2.9% higher than the SOTA methods in F1-Score, while
substantially outperforming SOTA methods in generalizability
Offline privacy preserving proxy re-encryption in mobile cloud computing
This paper addresses the always online behavior of the data owner in proxy re- encryption schemes for re-encryption keys issuing. We extend and adapt multi-authority ciphertext policy attribute based encryption techniques to type-based proxy re-encryption to build our solution. As a result, user authentication and user authorization are moved to the cloud server which does not require further interaction with the data owner, data owner and data users identities are hidden from the cloud server, and re-encryption keys are only issued to legitimate users. An in depth analysis shows that our scheme is secure, flexible and efficient for mobile cloud computing
Diagnostic prediction of complex diseases using phase-only correlation based on virtual sample template
Motivation: Complex diseases induce perturbations to interaction and regulation networks in living systems, resulting in dynamic equilibrium states that differ for different diseases and also normal states. Thus identifying gene expression patterns corresponding to different equilibrium states is of great benefit to the diagnosis and treatment of complex diseases. However, it remains a major challenge to deal with the high dimensionality and small size of available complex disease gene expression datasets currently used for discovering gene expression patterns.
Results: Here we present a phase-only correlation (POC) based classification method for recognizing the type of complex diseases. First, a virtual sample template is constructed for each subclass by averaging all samples of each subclass in a training dataset. Then the label of a test sample is determined by measuring the similarity between the test sample and each template. This novel method can detect the similarity of overall patterns emerged from the differentially expressed genes or proteins while ignoring small mismatches.
Conclusions: The experimental results obtained on seven publicly available complex disease datasets including microarray and protein array data demonstrate that the proposed POC-based disease classification method is effective and robust for diagnosing complex diseases with regard to the number of initially selected features, and its recognition accuracy is better than or comparable to other state-of-the-art machine learning methods. In addition, the proposed method does not require parameter tuning and data scaling, which can effectively reduce the occurrence of over-fitting and bias
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