Weakly-supervised Anomaly Detection

15 papers with code • 0 benchmarks • 1 datasets

This task has no description! Would you like to contribute one?

Libraries

Use these libraries to find Weakly-supervised Anomaly Detection models and implementations
2 papers
351

Most implemented papers

Deep Weakly-supervised Anomaly Detection

mala-lab/prenet 30 Oct 2019

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 22 May 2021

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 8 Mar 2022

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 9 Feb 2023

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 28 Sep 2023

Our approach takes into account snippet-level encoded features without the supervision of pseudo labels.

Explainable Deep Few-shot Anomaly Detection with Deviation Networks

Choubo/deviation-network-image 1 Aug 2021

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

tianyu0207/weakly-polyp 23 Mar 2022

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 25 Mar 2022

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 10 Aug 2022

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.