Weakly-supervised Anomaly Detection
14 papers with code • 0 benchmarks • 1 datasets
Benchmarks
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Libraries
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ShanghaiTech Campus
Most implemented papers
Deep Weakly-supervised Anomaly Detection
mala-lab/prenet
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To detect both seen and unseen anomalies, we introduce a novel deep weakly-supervised approach, namely Pairwise Relation prediction Network (PReNet), that learns pairwise relation features and anomaly scores by predicting the relation of any two randomly sampled training instances, in which the pairwise relation can be anomaly-anomaly, anomaly-unlabeled, or unlabeled-unlabeled.
Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detection
xuhongzuo/DeepOD
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Weakly-supervised anomaly detection aims at learning an anomaly detector from a limited amount of labeled data and abundant unlabeled data.
Diffusion Models for Medical Anomaly Detection
cian.unibas.ch/diffusion-anomaly
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In medical applications, weakly supervised anomaly detection methods are of great interest, as only image-level annotations are required for training.
Weakly Supervised Anomaly Detection: A Survey
yzhao062/wsad
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Anomaly detection (AD) is a crucial task in machine learning with various applications, such as detecting emerging diseases, identifying financial frauds, and detecting fake news.
Weakly-Supervised Video Anomaly Detection with Snippet Anomalous Attention
Daniel00008/WS-VAD-mindspore
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Our approach takes into account snippet-level encoded features without the supervision of pseudo labels.
Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection
jx-zhong-for-academic-purpose/GCN-Anomaly-Detection
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CVPR 2019
Remarkably, we obtain the frame-level AUC score of 82. 12% on UCF-Crime.
Explainable Deep Few-shot Anomaly Detection with Deviation Networks
Choubo/deviation-network-image
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Here, we study the problem of few-shot anomaly detection, in which we aim at using a few labeled anomaly examples to train sample-efficient discriminative detection models.
Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection
Current polyp detection methods from colonoscopy videos use exclusively normal (i. e., healthy) training images, which i) ignore the importance of temporal information in consecutive video frames, and ii) lack knowledge about the polyps.
Learning to Adapt to Unseen Abnormal Activities under Weak Supervision
junha-kim/Learning-to-Adapt-to-Unseen-Abnormal-Activities
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We present a meta-learning framework for weakly supervised anomaly detection in videos, where the detector learns to adapt to unseen types of abnormal activities effectively when only video-level annotations of binary labels are available.
Consistency-based Self-supervised Learning for Temporal Anomaly Localization
aimagelab/Consistency-based-Self-supervised-Learning-for-Temporal-Anomaly-Localization
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This work tackles Weakly Supervised Anomaly detection, in which a predictor is allowed to learn not only from normal examples but also from a few labeled anomalies made available during training.
