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A unifying review of deep and shallow anomaly detection
Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …
the art in detection performance on complex data sets, such as large collections of images or …
Recent advances in open set recognition: A survey
In real-world recognition/classification tasks, limited by various objective factors, it is usually
difficult to collect training samples to exhaust all classes when training a recognizer or …
difficult to collect training samples to exhaust all classes when training a recognizer or …
To trust or not to trust a classifier
Knowing when a classifier's prediction can be trusted is useful in many applications and
critical for safely using AI. While the bulk of the effort in machine learning research has been …
critical for safely using AI. While the bulk of the effort in machine learning research has been …
Two-stage learning to defer with multiple experts
We study a two-stage scenario for learning to defer with multiple experts, which is crucial in
practice for many applications. In this scenario, a predictor is derived in a first stage by …
practice for many applications. In this scenario, a predictor is derived in a first stage by …
Machine learning with a reject option: A survey
Abstract Machine learning models always make a prediction, even when it is likely to be
inaccurate. This behavior should be avoided in many decision support applications, where …
inaccurate. This behavior should be avoided in many decision support applications, where …
Learning with rejection
We introduce a novel framework for classification with a rejection option that consists of
simultaneously learning two functions: a classifier along with a rejection function. We …
simultaneously learning two functions: a classifier along with a rejection function. We …
Multi-class open set recognition using probability of inclusion
The perceived success of recent visual recognition approaches has largely been derived
from their performance on classification tasks, where all possible classes are known at …
from their performance on classification tasks, where all possible classes are known at …
Towards robust pattern recognition: A review
The accuracies for many pattern recognition tasks have increased rapidly year by year,
achieving or even outperforming human performance. From the perspective of accuracy …
achieving or even outperforming human performance. From the perspective of accuracy …
Training uncertainty-aware classifiers with conformalized deep learning
Deep neural networks are powerful tools to detect hidden patterns in data and leverage
them to make predictions, but they are not designed to understand uncertainty and estimate …
them to make predictions, but they are not designed to understand uncertainty and estimate …
Theoretically grounded loss functions and algorithms for score-based multi-class abstention
Learning with abstention is a key scenario where the learner can abstain from making a
prediction at some cost. In this paper, we analyze the score-based formulation of learning …
prediction at some cost. In this paper, we analyze the score-based formulation of learning …