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Found 3 papers, 1 papers with code
Improved Robustness to Open Set Inputs via Tempered Mixup
Supervised classification methods often assume that evaluation data is drawn from the same distribution as training data and that all classes are present for training.
Stream-51: Streaming Classification and Novelty Detection from Videos
In this work, we introduce Stream-51, a new dataset for streaming classification consisting of temporally correlated images from 51 distinct object categories and additional evaluation classes outside of the training distribution to test novelty recognition.
Are Out-of-Distribution Detection Methods Effective on Large-Scale Datasets?
We found that input perturbation and temperature scaling yield the best performance on large scale datasets regardless of the feature space regularization strategy.
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