Papers with Code 아카이브

Outlier Detection

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하위 태스크: 4
평가표: 11
보존된 평가 기록: 13

Outlier Detection is a task of identifying a subset of a given data set which are considered anomalous in that they are unusual from other instances. It is one of the core data mining tasks and is central to many applications. In the security field, it can be used to identify potentially threatening users, in the manufacturing field it can be used to identify parts that are likely to fail.

Source: Coverage-based Outlier Explanation

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/asjad99/MIMIC_RL_COACH

MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III

https://github.com/automl/SMAC3

DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning

https://github.com/baofff/BiSM

Learning Energy-Based Models in High-Dimensional Spaces with Multi-scale Denoising Score Matching

https://github.com/bayesian-object-tracking/dbot

Depth-Based Object Tracking Using a Robust Gaussian Filter

https://github.com/betterzhou/AAGNN

BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs

https://github.com/bondfeld/BNC_idioms

Classifying Idiomatic and Literal Expressions Using Topic Models and Intensity of Emotions

https://github.com/captainfffsama/pathcore

Towards Total Recall in Industrial Anomaly Detection

https://github.com/cavalli1234/AdaLAM

AdaLAM: Revisiting Handcrafted Outlier Detection

https://github.com/chickenbestlover/RNN-Time-series-Anomaly-Detection

LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection

https://github.com/chrvt/denoising-normalizing-flow

Probabilistic Autoencoder

https://github.com/clarken92/Mixed-variate-RBM

Outlier Detection on Mixed-Type Data: An Energy-based Approach

https://github.com/clberube/ip-vae

Data-driven modeling of time-domain induced polarization

https://github.com/dannyjameswilliams/truncsm

Estimating Density Models with Truncation Boundaries using Score Matching

https://github.com/dltan7/ECO-TR

ECO-TR: Efficient Correspondences Finding Via Coarse-to-Fine Refinement

https://github.com/dnguyen1196/word-embedding-cp

Word Embeddings via Tensor Factorization

https://github.com/drumpt/RotNet-OOD

Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty

https://github.com/elisejiuqizhang/RLMSAD

COPOD: Copula-Based Outlier Detection

https://github.com/englhardt/OneClassActiveLearning.jl

An Overview and a Benchmark of Active Learning for Outlier Detection with One-Class Classifiers

https://github.com/englhardt/SVDD.jl

An Overview and a Benchmark of Active Learning for Outlier Detection with One-Class Classifiers

https://github.com/epapagia/LocalVectors_AKE

Unsupervised Keyphrase Extraction from Scientific Publications

https://github.com/feedbackward/addro

Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty

https://github.com/fjchange/object_centric_VAD

Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Video

https://github.com/flaviagiammarino/encdec-ad-tensorflow

LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection

https://github.com/flaviagiammarino/lstm-fcn-pytorch

LSTM Fully Convolutional Networks for Time Series Classification

https://github.com/freedombenLiu/RNN-Time-series-Anomaly-Detection

LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection

https://github.com/freedombenLiu/https-github.com-chickenbestlover-RNN-Time-series-Anomaly-Detection

LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection

https://github.com/frgsimpson/kitt

https://github.com/gesina/bg_randomized_loce

Local Concept Embeddings for Analysis of Concept Distributions in Vision DNN Feature Spaces

https://github.com/gietema/ood-early-layer-detection

Detecting Out-of-Distribution Inputs in Deep Neural Networks Using an Early-Layer Output

https://github.com/gujingxiao/BaiduStar2020-Traffic-Sign-Detection-And-Pair-Competition-Solution

AdaLAM: Revisiting Handcrafted Outlier Detection

https://github.com/hangtingye/uadb

COPOD: Copula-Based Outlier Detection

https://github.com/hcw-00/PatchCore_anomaly_detection

Towards Total Recall in Industrial Anomaly Detection

https://github.com/heidelberg-hepml/normalized-autoencoders

Autoencoding Under Normalization Constraints

https://github.com/houshd/LSTM-FCN

LSTM Fully Convolutional Networks for Time Series Classification

https://github.com/houshd/MLSTM-FCN

LSTM Fully Convolutional Networks for Time Series Classification

https://github.com/hs-jiang/FISVDD

Fast Incremental SVDD Learning Algorithm with the Gaussian Kernel

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.