Open-Set Recognition Using Intra-Class Splitting
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Updated
Oct 11, 2019 - Python
Open-Set Recognition Using Intra-Class Splitting
CVPR 2019: Ranked List Loss for Deep Metric Learning, with extension for TPAMI submission
A toolbox for one-class classification and open set recognition based on intra-class splitting
[CVPR 2021] Few-shot Open-set Recognition by Transformation Consistency
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.
The code repository for "Learning Placeholders for Open-Set Recognition" (CVPR'21 Oral) in PyTorch.
Open Set Recognition
Source code for baseline obtenience
[ECCV 2020] Pytorch codes for Open-set Adversarial Defense
[IJCV 2022] Pytorch codes for Open-set Adversarial Defense with Clean-Adversarial Mutual Learning
Open-source code for our paper: Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition
Official PyTorch implementation of the paper “Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection”, open-set anomaly detection, few-shot anomaly detection, semi-supervised anomaly detection.
This is a novel unknown sar target identification method based on feature extraction networks and KLD-RPA joint discrimination. Experiment results form MSTAR dataset shows that our proposed Fea-DA achieves state of the art unknown sar target identification accuracy while maintaining the high recognition accuracy of known target.
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".
Open-Set Support Vector Machines (OSSVM) [see commit message https://github.com/pedrormjunior/ossvm/commit/50d51dc482c8e13df7d9037976b97db7e60a1ccf for usage]
Machine learning project conducted together with Volvo Cars
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.
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.
The Official Repository for "Generalized OOD Detection: A Survey"
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