Open Set Learning
88 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
Most implemented papers
Distill-SODA: Distilling Self-Supervised Vision Transformer for Source-Free Open-Set Domain Adaptation in Computational Pathology
Developing computational pathology models is essential for reducing manual tissue typing from whole slide images, transferring knowledge from the source domain to an unlabeled, shifted target domain, and identifying unseen categories.
Pairwise Similarity Learning is SimPLE
ydwen/opensphere
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ICCV 2023
In this paper, we focus on a general yet important learning problem, pairwise similarity learning (PSL).
Open-Set Image Tagging with Multi-Grained Text Supervision
xinyu1205/recognize-anything
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Specifically, for predefined commonly used tag categories, RAM++ showcases 10. 2 mAP and 15. 4 mAP enhancements over CLIP on OpenImages and ImageNet.
Sparse Representation-based Open Set Recognition
We propose a generalized Sparse Representation- based Classification (SRC) algorithm for open set recognition where not all classes presented during testing are known during training.
AP18-OLR Challenge: Three Tasks and Their Baselines
The third oriental language recognition (OLR) challenge AP18-OLR is introduced in this paper, including the data profile, the tasks and the evaluation principles.
Classification-Reconstruction Learning for Open-Set Recognition
Existing open-set classifiers rely on deep networks trained in a supervised manner on known classes in the training set; this causes specialization of learned representations to known classes and makes it hard to distinguish unknowns from knowns.
An Open-set Recognition and Few-Shot Learning Dataset for Audio Event Classification in Domestic Environments
machine-listeners-valencia/fsl_osr_dataset_baseline
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This paper is aimed at poviding the audio recognition community with a carefully annotated dataset (https://zenodo. org/record/3689288) for FSL in an OSR context comprised of 1360 clips from 34 classes divided into pattern sounds} and unwanted sounds.
Conditional Gaussian Distribution Learning for Open Set Recognition
loganriggs/conditionalGaussionRecreation
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CVPR 2020
A typical challenge is that unknown samples may be fed into the system during the testing phase and traditional deep neural networks will wrongly recognize the unknown sample as one of the known classes.
Class Anchor Clustering: a Loss for Distance-based Open Set Recognition
We also show that our anchored class centres achieve higher open set performance than learnt class centres, particularly on object-based datasets and large numbers of training classes.
Few-Shot Open-Set Recognition using Meta-Learning
BoLiu-SVCL/meta-open
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CVPR 2020
It is argued that the classic softmax classifier is a poor solution for open-set recognition, since it tends to overfit on the training classes.
