Open Set Learning
90 papers with code • 0 benchmarks • 6 datasets
Traditional supervised learning aims to train a classifier in the closed-set world, where training and test samples share the same label space. Open set learning (OSL) is a more challenging and realistic setting, where there exist test samples from the classes that are unseen during training. Open set recognition (OSR) is the sub-task of detecting test samples which do not come from the training.
Benchmarks
These leaderboards are used to track progress in Open Set Learning
Libraries
Use these libraries to find Open Set Learning models and implementations
MIMII
NINCO
ImageNet-1k vs NINCO
UCCS
Latest papers with no code
Revealing the Two Sides of Data Augmentation: An Asymmetric Distillation-based Win-Win Solution for Open-Set Recognition
In this paper, we reveal the two sides of data augmentation: enhancements in closed-set recognition correlate with a significant decrease in open-set recognition.
Open-Set Video-based Facial Expression Recognition with Human Expression-sensitive Prompting
In Video-based Facial Expression Recognition (V-FER), models are typically trained on closed-set datasets with a fixed number of known classes.
Know Yourself Better: Diverse Discriminative Feature Learning Improves Open Set Recognition
In this paper, we conduct an analysis of open set recognition methods, focusing on the aspect of feature diversity.
Out-of-Distribution Data: An Acquaintance of Adversarial Examples -- A Survey
Finally, we highlight the limitations of the existing work and propose promising research directions that explore adversarial and OOD inputs within a unified framework.
Open-Set Recognition in the Age of Vision-Language Models
Are vision-language models (VLMs) open-set models because they are trained on internet-scale datasets?
ROG$_{PL}$: Robust Open-Set Graph Learning via Region-Based Prototype Learning
In specific, ROG$_{PL}$ consists of two modules, i. e., denoising via label propagation and open-set prototype learning via regions.
Taking Class Imbalance Into Account in Open Set Recognition Evaluation
In recent years Deep Neural Network-based systems are not only increasing in popularity but also receive growing user trust.
All Beings Are Equal in Open Set Recognition
In open-set recognition (OSR), a promising strategy is exploiting pseudo-unknown data outside given $K$ known classes as an additional $K$+$1$-th class to explicitly model potential open space.
Open-Set Facial Expression Recognition
Facial expression recognition (FER) models are typically trained on datasets with a fixed number of seven basic classes.
A Survey on Open-Set Image Recognition
Open-set image recognition (OSR) aims to both classify known-class samples and identify unknown-class samples in the testing set, which supports robust classifiers in many realistic applications, such as autonomous driving, medical diagnosis, security monitoring, etc.
