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
Latest papers
Exploring Diverse Representations for Open Set Recognition
vanixxz/medaf
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We show that the differences in attention maps can lead to diverse representations so that the fused representations can well handle the open space.
Advancing Image Retrieval with Few-Shot Learning and Relevance Feedback
With such a massive growth in the number of images stored, efficient search in a database has become a crucial endeavor managed by image retrieval systems.
Navigating Open Set Scenarios for Skeleton-based Action Recognition
kpeng9510/os-sar
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In real-world scenarios, human actions often fall outside the distribution of training data, making it crucial for models to recognize known actions and reject unknown ones.
Unified Classification and Rejection: A One-versus-All Framework
zhen-cheng121/cpn_ova_unified
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Previous methods mostly take post-training score transformation or hybrid models to ensure low scores on OOD inputs while separating known classes.
Open-Set Face Recognition with Maximal Entropy and Objectosphere Loss
rafaelvareto/maximal-entropy-loss
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MEL modifies the traditional Cross-Entropy loss in favor of increasing the entropy for negative samples and attaches a penalty to known target classes in pursuance of gallery specialization.
Open-Set Image Tagging with Multi-Grained Text Supervision
xinyu1205/Recognize_Anything-Tag2Text
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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.
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).
OpenIncrement: A Unified Framework for Open Set Recognition and Deep Class-Incremental Learning
In most works on deep incremental learning research, it is assumed that novel samples are pre-identified for neural network retraining.
Domain Adaptive Few-Shot Open-Set Learning
debabratapal7/dafosnet
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ICCV 2023
Few-shot learning has made impressive strides in addressing the crucial challenges of recognizing unknown samples from novel classes in target query sets and managing visual shifts between domains.
Open-set Face Recognition with Neural Ensemble, Maximal Entropy Loss and Feature Augmentation
rafaelvareto/maximal-entropy-loss
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Open-set face recognition refers to a scenario in which biometric systems have incomplete knowledge of all existing subjects.
