open-set classification
14 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in open-set classification
UCCS
Most implemented papers
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.
Unbiased Evaluation of Deep Metric Learning Algorithms
Deep metric learning (DML) is a popular approach for images retrieval, solving verification (same or not) problems and addressing open set classification.
Open Set Authorship Attribution toward Demystifying Victorian Periodicals
Existing research in computational authorship attribution (AA) has primarily focused on attribution tasks with a limited number of authors in a closed-set configuration.
Representative-Discriminative Learning for Open-set Land Cover Classification of Satellite Imagery
raziehkaviani/rdosr
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ECCV 2020
Although inherently a classification problem, both representative and discriminative aspects of data need to be exploited in order to better distinguish unknown classes from known.
Large-Scale Open-Set Classification Protocols for ImageNet
Open-Set Classification (OSC) intends to adapt closed-set classification models to real-world scenarios, where the classifier must correctly label samples of known classes while rejecting previously unseen unknown samples.
Open-Set Automatic Target Recognition
Automatic Target Recognition (ATR) is a category of computer vision algorithms which attempts to recognize targets on data obtained from different sensors.
Text Classification in the Wild: a Large-scale Long-tailed Name Normalization Dataset
In this work, we first collect a large-scale institution name normalization dataset LoT-insts1, which contains over 25k classes that exhibit a naturally long-tailed distribution.
Learning Pairwise Interaction for Generalizable DeepFake Detection
We obtain 98. 48% BOSC accuracy on the FF++ dataset and 90. 87% BOSC accuracy on the CelebDF dataset suggesting a promising direction for generalization of DeepFake detection.
ProTeCt: Prompt Tuning for Taxonomic Open Set Classification
A new Prompt Tuning for Hierarchical Consistency (ProTeCt) technique is then proposed to calibrate classification across label set granularities.
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.
