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Out-of-Distribution Detection

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하위 태스크: 0
평가표: 53
보존된 평가 기록: 269

Detect out-of-distribution or anomalous examples.

코드

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

https://github.com/rushilanirudh/amp

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Out of Distribution Detection via Neural Network Anchoring

https://github.com/rux001/Awesome-LLM-Anomaly-OOD-Detection

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Large Language Models for Anomaly and Out-of-Distribution Detection: A Survey

https://github.com/rvl-lab-utoronto/dibs

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Diversity inducing Information Bottleneck in Model Ensembles

https://github.com/rwl93/bnp4ood

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A Bayesian Nonparametric Perspective on Mahalanobis Distance for Out of Distribution Detection

https://github.com/saandeepa93/FlowCon_OOD

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FlowCon: Out-of-Distribution Detection using Flow-Based Contrastive Learning

https://github.com/saehyung-lee/hftt

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Textual Training for the Hassle-Free Removal of Unwanted Visual Data: Case Studies on OOD and Hateful Image Detection

https://github.com/samd770/generative-models-knowledge

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Approximations to the Fisher Information Metric of Deep Generative Models for Out-Of-Distribution Detection

https://github.com/selflein/EBM-OOD-Detection

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On Out-of-distribution Detection with Energy-based Models

https://github.com/selflein/ma-ebm

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Master's Thesis: Out-of-distribution Detection with Energy-based Models

https://github.com/servicenow/broad-openood

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Expecting The Unexpected: Towards Broad Out-Of-Distribution Detection

https://github.com/sesmae/zoc

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Zero-Shot Out-of-Distribution Detection Based on the Pre-trained Model CLIP

https://github.com/sgvaze/osr_closed_set_all_you_need

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Open-Set Recognition: a Good Closed-Set Classifier is All You Need?

https://github.com/shinypond/ood_detection

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Robust Out-of-Distribution Detection on Deep Probabilistic Generative Models

https://github.com/shuolucs/awesome-out-of-distribution-detection

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Recent Advances in OOD Detection: Problems and Approaches

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/simonklind/nf-gradients

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Making the Flow Glow -- Robot Perception under Severe Lighting Conditions using Normalizing Flow Gradients

https://github.com/snavalm/ifl_unsup_anom_det

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Implicit field learning for unsupervised anomaly detection in medical images

https://github.com/snavalm/mood22

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Achieving state-of-the-art performance in the Medical Out-of-Distribution (MOOD) challenge using plausible synthetic anomalies

https://github.com/sndnyang/GMMC

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Generative Max-Mahalanobis Classifiers for Image Classification, Generation and More

https://github.com/sndnyang/sadajem

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Towards Bridging the Performance Gaps of Joint Energy-based Models

https://github.com/snowood1/wenn

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Multidimensional Uncertainty-Aware Evidential Neural Networks

https://github.com/ssskj/evinet

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EviNet: Evidential Reasoning Network for Resilient Graph Learning in the Open and Noisy Environments

https://github.com/stanislavfort/adversaries_to_ood_detection

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Adversarial vulnerability of powerful near out-of-distribution detection

https://github.com/stomach-ache/long-tailed-ood-detection

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EAT: Towards Long-Tailed Out-of-Distribution Detection

https://github.com/sverneka/ConfidentClassifierICLR19

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Analysis of Confident-Classifiers for Out-of-distribution Detection

https://github.com/sverneka/OODGen

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Out-of-distribution Detection in Classifiers via Generation

https://github.com/tahabelkhouja/srs

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Out-of-Distribution Detection in Time-Series Domain: A Novel Seasonal Ratio Scoring Approach

https://github.com/tajwarfahim/ood_detection_inconsistency

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No True State-of-the-Art? OOD Detection Methods are Inconsistent across Datasets

https://github.com/taldatech/soft-intro-vae-pytorch

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Soft-IntroVAE: Analyzing and Improving the Introspective Variational Autoencoder

https://github.com/taralloc/st-rkm-ood

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Unsupervised Energy-based Out-of-distribution Detection using Stiefel-Restricted Kernel Machine

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/testingautomated-usi/surprise-adequacy

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A Review and Refinement of Surprise Adequacy

https://github.com/tim-learn/ueo

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Realistic Unsupervised CLIP Fine-tuning with Universal Entropy Optimization

https://github.com/tkasarla/max-separation-as-inductive-bias

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Maximum Class Separation as Inductive Bias in One Matrix

https://github.com/tmlr-group/atol

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Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources

https://github.com/tmlr-group/dal

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Learning to Augment Distributions for Out-of-Distribution Detection

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