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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/CameronTaylorFL/stam

공식 코드

Unsupervised Progressive Learning and the STAM Architecture

https://github.com/JWTHI/ViTAL-SCENE

공식 코드

Novelty Detection and Analysis of Traffic Scenario Infrastructures in the Latent Space of a Vision Transformer-Based Triplet Autoencoder

https://github.com/Niousha12/Puzzle_Anomaly_Detection

공식 코드

Puzzle-AE: Novelty Detection in Images through Solving Puzzles

https://github.com/PramuPerera/DeepOneClass

공식 코드

Learning Deep Features for One-Class Classification

https://github.com/PramuPerera/TransferLearningNovelty

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Deep Transfer Learning for Multiple Class Novelty Detection

https://github.com/ROBOTICSENGINEER/Open_World_Learning_Without_Labels

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A Review of Open-World Learning and Steps Toward Open-World Learning Without Labels

https://github.com/Salomon2000Sedjro/MasterThesisAIMS

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Efficient Novelty Detection Methods for Early Warning of Potential Fatal Diseases

https://github.com/VAlex22/ND_VAE

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q-Space Novelty Detection with Variational Autoencoders

https://github.com/VeriXAI/Outside-the-Box

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Outside the Box: Abstraction-Based Monitoring of Neural Networks

https://github.com/VisionLearningGroup/OP_Match

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OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers

https://github.com/aimagelab/novelty-detection

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Latent Space Autoregression for Novelty Detection

https://github.com/alinlab/CSI

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CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances

https://github.com/antoalli/3d_os

공식 코드

3DOS: Towards 3D Open Set Learning -- Benchmarking and Understanding Semantic Novelty Detection on Point Clouds

https://github.com/arianemarandon/adadetect

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Adaptive novelty detection with false discovery rate guarantee

https://github.com/bit-ml/Stylist

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Robust Novelty Detection through Style-Conscious Feature Ranking

https://github.com/bychelsea/vand-april-gan

공식 코드

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/caoyunkang/segment-any-anomaly

공식 코드

2nd Place Winning Solution for the CVPR2023 Visual Anomaly and Novelty Detection Challenge: Multimodal Prompting for Data-centric Anomaly Detection

https://github.com/cvjena/semantic-embeddings

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Hierarchy-based Image Embeddings for Semantic Image Retrieval

https://github.com/datasig-ac-uk/signova

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Novelty Detection on Radio Astronomy Data using Signatures

https://github.com/dnguyengithub/AudioNovelty

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Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty Detection

https://github.com/donaldrr/simplenet

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SimpleNet: A Simple Network for Image Anomaly Detection and Localization

https://github.com/ericpts/reto

공식 코드

Semi-supervised novelty detection using ensembles with regularized disagreement

https://github.com/francescocappio/resend

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Semantic Novelty Detection via Relational Reasoning

https://github.com/hq-deng/RD4AD

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Anomaly Detection via Reverse Distillation from One-Class Embedding

https://github.com/htdt/lwm

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Latent World Models For Intrinsically Motivated Exploration

https://github.com/iskandor/snd

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

https://github.com/jcl823/maw

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Novelty Detection via Robust Variational Autoencoding

https://github.com/jh-jeong/nuisance_ib

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Enhancing Multiple Reliability Measures via Nuisance-extended Information Bottleneck

https://github.com/jingkang50/oodsurvey

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Generalized Out-of-Distribution Detection: A Survey

https://github.com/jingkang50/openood

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Generalized Out-of-Distribution Detection: A Survey

https://github.com/kampmichael/noveltydetectionsequentialdata

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Novelty Detection in Sequential Data by Informed Clustering and Modeling

https://github.com/khalooei/ALOCC-CVPR2018

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Adversarially Learned One-Class Classifier for Novelty Detection

https://github.com/kpeng9510/os-sar

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Navigating Open Set Scenarios for Skeleton-based Action Recognition

https://github.com/lifelong-ml/shels

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SHELS: Exclusive Feature Sets for Novelty Detection and Continual Learning Without Class Boundaries

https://github.com/mesarcik/nln

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Improving Novelty Detection using the Reconstructions of Nearest Neighbours

https://github.com/meshiba/derandomized-novelty-detection

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Derandomized Novelty Detection with FDR Control via Conformal E-values

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