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Outlier Detection

28 papers with code · Methodology

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PyOD: A Python Toolbox for Scalable Outlier Detection

6 Jan 2019yzhao062/pyod

PyOD is an open-source Python toolbox for performing scalable outlier detection on multivariate data.

ANOMALY DETECTION OUTLIER ENSEMBLES

Multi-Person Pose Estimation with Local Joint-to-Person Associations

30 Aug 2016MVIG-SJTU/RMPE

To this end, we consider multi-person pose estimation as a joint-to-person association problem.

MULTI-PERSON POSE ESTIMATION OUTLIER DETECTION

Adversarially Learned One-Class Classifier for Novelty Detection

CVPR 2018 khalooei/ALOCC-CVPR2018

Our architecture is composed of two deep networks, each of which trained by competing with each other while collaborating to understand the underlying concept in the target class, and then classify the testing samples.

ANOMALY DETECTION ONE-CLASS CLASSIFIER

Deep Sets

NeurIPS 2017 lwtnn/lwtnn

Our main theorem characterizes the permutation invariant functions and provides a family of functions to which any permutation invariant objective function must belong.

ANOMALY DETECTION OUTLIER DETECTION

Generative Adversarial Active Learning for Unsupervised Outlier Detection

28 Sep 2018leibinghe/GAAL-based-outlier-detection

In this paper, we approach outlier detection as a binary-classification issue by sampling potential outliers from a uniform reference distribution.

ACTIVE LEARNING OUTLIER DETECTION

Depth-Based Object Tracking Using a Robust Gaussian Filter

19 Feb 2016bayesian-object-tracking/dbot

To address this issue, we show how a recently published robustification method for Gaussian filters can be applied to the problem at hand.

OBJECT TRACKING OUTLIER DETECTION

Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos

CVPR 2019 RomeroBarata/skeleton_based_anomaly_detection

Appearance features have been widely used in video anomaly detection even though they contain complex entangled factors.

ANOMALY DETECTION OUTLIER DETECTION

Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning

ICLR 2019 dccastro/Morpho-MNIST

Revealing latent structure in data is an active field of research, having introduced exciting technologies such as variational autoencoders and adversarial networks, and is essential to push machine learning towards unsupervised knowledge discovery.

DOMAIN ADAPTATION OUTLIER DETECTION REPRESENTATION LEARNING

Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection

26 Jun 2019twistedcubic/que-outlier-detection

In robust mean estimation the goal is to estimate the mean $\mu$ of a distribution on $\mathbb{R}^d$ given $n$ independent samples, an $\varepsilon$-fraction of which have been corrupted by a malicious adversary.

OUTLIER DETECTION

STWalk: Learning Trajectory Representations in Temporal Graphs

11 Nov 2017supriya-pandhre/STWalk

In this paper, we present a novel approach, STWalk, for learning trajectory representations of nodes in temporal graphs.

OUTLIER DETECTION