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Deep neural networks have achieved outstanding performance over various tasks, but they have a critical issue: over-confident predictions even for completely unknown samples.
Deep cnn-based multi-task learning for open-set recognition
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Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
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Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016
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Generative openmax for multi-class open set classification
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Selective classification for deep neural networks
Geifman, Y., & El-Yaniv, R. (2017) · 2017
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
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Likelihood ratios for out-of-distribution detection
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Out-of-distribution detection in classifiers via generation
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Deep convolutional neural networks for image classification: A comprehensive review
Rawat, W., & Wang, Z. (2017) · 2017
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Salimans, T., Karpathy, A., Chen, X., & Kingma, D. P. (2017) · 2017
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Learning confidence for out-of-distribution detection in neural networks
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To trust or not to trust a classifier
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Generative-discriminative feature representations for open-set recognition
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Are open set classification methods effective on large-scale datasets?
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Conditional gaussian distribution learning for open set recognition
Sun, X., Yang, Z., Zhang, C., Ling, K.-V., & Peng, G. (2020) · 2020
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Csi: Novelty detection via contrastive learning on distributionally shifted instances
Tack, J., Mo, S., Jeong, J., & Shin, J. (2020) · 2020
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Large image datasets: A pyrrhic win for computer vision?
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Confidence estimation via auxiliary models
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React: Out-of-distribution detection with rectified activations
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No true state-of-the-art? ood detection methods are inconsistent across datasets
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