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Novelty Detection

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

https://github.com/mfrashad/creativegan

공식 코드

CreativeGAN: Editing Generative Adversarial Networks for Creative Design Synthesis

https://github.com/mmasana/OoD_Mining

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Metric Learning for Novelty and Anomaly Detection

https://github.com/mojtaba-nafez/unode

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Universal Novelty Detection Through Adaptive Contrastive Learning

https://github.com/nianzuma/pat-snd

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Semantic Novelty Detection and Characterization in Factual Text Involving Named Entities

https://github.com/nicolamelluso/science-novelty

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Beyond Citations: Measuring Novel Scientific Ideas and their Impact in Publication Text

https://github.com/nikitazozoulenko/kernel-timeseries-anomaly-detection

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Infinite-dimensional Mahalanobis Distance with Applications to Kernelized Novelty Detection

https://github.com/otkupjnoz/oc-cnn

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One-Class Convolutional Neural Network

https://github.com/pic4ser/unsupervised-novelty-detection-methods-benchmarking-with-wavelet-decomposition

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Unsupervised Novelty Detection Methods Benchmarking with Wavelet Decomposition

https://github.com/podgorskiy/GPND

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Generative Probabilistic Novelty Detection with Adversarial Autoencoders

https://github.com/proxymallick/OpenDet_CWA

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Wasserstein Distance-based Expansion of Low-Density Latent Regions for Unknown Class Detection

https://github.com/rahafaljunditme/continual-novelty-detection

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Continual Novelty Detection

https://github.com/raun1/ASC-NET

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ASC-Net : Adversarial-based Selective Network for Unsupervised Anomaly Segmentation

https://github.com/rmazzier/OpenSetGaitRecognition_PCAA

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Open-Set Gait Recognition from Sparse mmWave Radar Point Clouds

https://github.com/rohban-lab/Salehi_submitted_2020

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ARAE: Adversarially Robust Training of Autoencoders Improves Novelty Detection

https://github.com/rohban-lab/fitymi

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Fake It Till You Make It: Towards Accurate Near-Distribution Novelty Detection

https://github.com/rohban-lab/zarnd

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Killing it with Zero-Shot: Adversarially Robust Novelty Detection

https://github.com/shaoyuanlo/principals

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Adversarially Robust One-class Novelty Detection

https://github.com/silviogalesso/dense-ood-knns

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Far Away in the Deep Space: Dense Nearest-Neighbor-Based Out-of-Distribution Detection

https://github.com/smidl/AnomalyDetection.jl

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Are generative deep models for novelty detection truly better?

https://github.com/smkia/DNM

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Neural Processes Mixed-Effect Models for Deep Normative Modeling of Clinical Neuroimaging Data

https://github.com/smsd75/redefining_normal_accv24

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Redefining Normal: A Novel Object-Level Approach for Multi-Object Novelty Detection

https://github.com/taslimisina/osr-ood-ad-methods

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A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges

https://github.com/tnlin/SMDN

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A Post-processing Method for Detecting Unknown Intent of Dialogue System via Pre-trained Deep Neural Network Classifier

https://github.com/torchdrift/partial-wasserstein

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Partial Wasserstein and Maximum Mean Discrepancy distances for bridging the gap between outlier detection and drift detection

https://github.com/tufts-ai-robotics-group/polycraft-novelty-data

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NovelCraft: A Dataset for Novelty Detection and Discovery in Open Worlds

https://github.com/ulyssegazin/TransductiveAdaptive_CP

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Transductive conformal inference with adaptive scores

https://github.com/wang22ti/openauc

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OpenAUC: Towards AUC-Oriented Open-Set Recognition

https://github.com/xaggi/OGNet

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Old is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm

https://github.com/xiaoyuanguo/tend_medicalnoveltydetection

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Margin-Aware Intra-Class Novelty Identification for Medical Images

https://gitlab.com/artelabsuper/ocdmst

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OCmst: One-class Novelty Detection using Convolutional Neural Network and Minimum Spanning Trees

https://github.com/1010code/OneClass_NeuralNetwork

One-Class Convolutional Neural Network

https://github.com/Ars235/Novelty_Detection

Adversarially Learned One-Class Classifier for Novelty Detection

https://github.com/ErikKratzCth/ALOCC_Keras_SMILE

Adversarially Learned One-Class Classifier for Novelty Detection

https://github.com/Goulustis/One_Class_MNIST

Learning Deep Features for One-Class Classification

https://github.com/JuneKyu/CLAD

One-Class Convolutional Neural Network

https://github.com/Merenguelkl/Reverse_Disstilation

Anomaly Detection via Reverse Distillation from One-Class Embedding

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