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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/PINTO0309/Keras-OneClassAnomalyDetection

Learning Deep Features for One-Class Classification

https://github.com/Pavyel/Novelty-Detection

TAP-DLND 1.0 : A Corpus for Document Level Novelty Detection

https://github.com/SimonThomine/DistillationAD

Anomaly Detection via Reverse Distillation from One-Class Embedding

https://github.com/SimonThomine/RememberingNormality

Anomaly Detection via Reverse Distillation from One-Class Embedding

https://github.com/Tony607/ALOCC_Keras

Adversarially Learned One-Class Classifier for Novelty Detection

https://github.com/YuvalBahat/Confidence_From_Invariance

Confidence from Invariance to Image Transformations

https://github.com/adverml/advopenood

Generalized Out-of-Distribution Detection: A Survey

https://github.com/antoinedemathelin/openood

Generalized Out-of-Distribution Detection: A Survey

https://github.com/aotumanbiu/OC-NN

One-Class Convolutional Neural Network

https://github.com/buiduchanh/Learning-Deep-Features-for-One-Class-Classification

Learning Deep Features for One-Class Classification

https://github.com/cair/PyTsetlinMachineCUDA

Measuring the Novelty of Natural Language Text Using the Conjunctive Clauses of a Tsetlin Machine Text Classifier

https://github.com/cair/TsetlinMachine

Measuring the Novelty of Natural Language Text Using the Conjunctive Clauses of a Tsetlin Machine Text Classifier

https://github.com/cair/pyTsetlinMachine

Measuring the Novelty of Natural Language Text Using the Conjunctive Clauses of a Tsetlin Machine Text Classifier

https://github.com/cair/pyTsetlinMachineMT

Measuring the Novelty of Natural Language Text Using the Conjunctive Clauses of a Tsetlin Machine Text Classifier

https://github.com/cair/pyTsetlinMachineParallel

Measuring the Novelty of Natural Language Text Using the Conjunctive Clauses of a Tsetlin Machine Text Classifier

https://github.com/ckinateder/pytsetlinmachineparallel

Word-level Human Interpretable Scoring Mechanism for Novel Text Detection Using Tsetlin Machines

https://github.com/codelion/adaptive-classifier

AquaSignal: An Integrated Framework for Robust Underwater Acoustic Analysis

https://github.com/edithal-14/A-Deep-Neural-Solution-To-Document-Level-Novelty-Detection-COLING-2018-

TAP-DLND 1.0 : A Corpus for Document Level Novelty Detection

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

Efficient SVDD Sampling with Approximation Guarantees for the Decision Boundary

https://github.com/englhardt/ocs-evaluation

Efficient SVDD Sampling with Approximation Guarantees for the Decision Boundary

https://github.com/hq-deng/AnoVL

APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot AD

https://github.com/jcoreyes/ex2

EX2: Exploration with Exemplar Models for Deep Reinforcement Learning

https://github.com/jewelltaylor/OLED

OLED: One-Class Learned Encoder-Decoder Network with Adversarial Context Masking for Novelty Detection

https://github.com/kzkadc/alocc_mnist

Adversarially Learned One-Class Classifier for Novelty Detection

https://github.com/llllxt/oneclass-deep-feature

Learning Deep Features for One-Class Classification

https://github.com/lucaerculiani/towards-visual-semantics

Continual egocentric object recognition

https://github.com/michalnand/reinforcement_learning

Self-supervised network distillation: an effective approach to exploration in sparse reward environments

https://github.com/morphocluster/morphocluster

MorphoCluster: Efficient Annotation of Plankton images by Clustering

https://github.com/openvinotoolkit/anomalib/tree/development/anomalib/models/reverse_distillation

Anomaly Detection via Reverse Distillation from One-Class Embedding

https://github.com/septmars/DL

SimpleNet: A Simple Network for Image Anomaly Detection and Localization

https://github.com/thuiar/DeepUnkID

Deep Unknown Intent Detection with Margin Loss

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

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

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