A curated list of papers & resources linked to open set recognition, out-of-distribution, open set domain adaptation and open world recognition
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Updated
Mar 1, 2024
A curated list of papers & resources linked to open set recognition, out-of-distribution, open set domain adaptation and open world recognition
Benchmarking Generalized Out-of-Distribution Detection
Video Foundation Models & Data for Multimodal Understanding
The Official Repository for "Generalized OOD Detection: A Survey"
Open Set Recognition
[TPAMI 2021] Adversarial Reciprocal Points Learning for Open Set Recognition
Papers for Open Knowledge Discovery
👽 Out-of-Distribution Detection with PyTorch
S. Liu, Q. Shi and L. Zhang, "Few-Shot Hyperspectral Image Classification With Unknown Classes Using Multitask Deep Learning," in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2020.3018879.
Open-source code for our paper: Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition
A project to add scalable state-of-the-art out-of-distribution detection (open set recognition) support by changing two lines of code! Perform efficient inferences (i.e., do not increase inference time) and detection without classification accuracy drop, hyperparameter tuning, or collecting additional data.
The code repository for "Learning Placeholders for Open-Set Recognition" (CVPR'21 Oral) in PyTorch.
CVPR 2019: Ranked List Loss for Deep Metric Learning, with extension for TPAMI submission
This is the official repository for the paper "A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges".
Official code for RbA: Segmenting Unknown Regions Rejected by All (ICCV 2023)
Code release for Mind the Gap: Enlarging the Domain Gap in Open Set Domain Adaptation (TCSVT 2023)
A project to improve out-of-distribution detection (open set recognition) and uncertainty estimation by changing a few lines of code in your project! Perform efficient inferences (i.e., do not increase inference time) without repetitive model training, hyperparameter tuning, or collecting additional data.
[CVPR 2023] Glocal Energy-based Learning for Few-Shot Open-Set Recognition
Open set classification of car models. This 3-step classifier solves the problem where dogs are classified as cars, by first filtering these images out using ResNet CNNs transfer-trained on different datasets.
Official implementation of KDD'23 paper "Deep Weakly-supervised Anomaly Detection"
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