Out-of-Distribution Detection with Deep Nearest Neighbors

Sun, Yiyou; Yifei, Ming,; Zhu, Xing; Li, Yixuan

doi:10.48550/arxiv.2204.06507

Cited by 5 publications

(10 citation statements)

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“…Under distributional assumptions free hypothesis, ODD needs the right tuning of some parameters [8]. Our solution still maintains the former and does not rely on any critical parameter setting.…”

Section: Related Workmentioning

confidence: 88%

“…If the data at runtime generates a histogram "significantly different" from the training one, it means that the data are OoD. Unlike K-NN [8] and Neural Networks distance [9], where a single distance criterion is defined, the similarity measure can be derived through multiple metrics. This offers support to the tests mentioned by EASA, since the proposed method measures incremental cases of departure from in-distribution.…”

Section: A Contributionmentioning

confidence: 99%

“…Most of the solutions proposed to address the problem of the OoD make strong distributional assumptions of the feature space [10] or suppose they are given a training in and out probability density function (pdf) [11], but this not always holds in practice. What is more, lots of statistic tests fail to estimate the real distribution of training data (data are not enough and the pdf are too coarse) [8]. Some methods, widely used across OoD detection on images are the Maximum Softmax Probability [12], the ODIN [13] and the Energybased OoD Detection [14], while others use outlier methods as the Isolation Forest [15].…”

Section: Related Workmentioning

confidence: 99%

“…Although the literature in the field of OoD already poses solutions based on labelled 1 Generalization bounds, see, e.g., [1], concern the gap that exists between the empirical risk, calculated on the data actually available (on which the model is trained) and the theoretical risk, calculated on the distribution of probability that represents the data; this probability distribution is unknown in closed-form and, in the ODD context, represents the "in-distribution". data or through anomaly detection, as evidenced by [7], [8], the OoD according to distributional assumption-free and OoDagnostic criteria is still an open problem 2 .…”

Section: Introductionmentioning

confidence: 99%

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Rule-based Out-Of-Distribution Detection

Bernardi¹,

Narteni²,

Cambiaso³

et al. 2023

Preprint

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Out-of-distribution detection is one of the most critical issue in the deployment of machine learning. The data analyst must assure that data in operation should be compliant with the training phase as well as understand if the environment has changed in a way that autonomous decisions would not be safe anymore. The method of the paper is based on eXplainable Artificial Intelligence (XAI); it takes into account different metrics to identify any resemblance between in-distribution and out of, as seen by the XAI model. The approach is non-parametric and distributional assumption free. The validation over complex scenarios (predictive maintenance, vehicle platooning, covert channels in cybersecurity) corroborates both precision in detection and evaluation of training-operation conditions proximity.

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Section: Related Workmentioning

confidence: 88%

Section: A Contributionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Rule-based Out-Of-Distribution Detection

Bernardi¹,

Narteni²,

Cambiaso³

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…The work in [97] measures the Mahalanobis distance to class centroids to detect OOD. The study in [98] uses the nearest-neighbor distance for OOD detection. Note that the approach in this chapter is complementary to the latest OOD detection methods.…”

Section: Related Workmentioning

confidence: 99%

Improving security of autonomous cyber-physical systems against adversarial examples

Song¹

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AI-based model for T1-weighted brain MRI diagnoses Amyotrophic Lateral Sclerosis

Turrisi,

Forzanini,

Stanziano

et al. 2024

Preprint

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Amyotrophic Lateral Sclerosis (ALS) is an incurable deadly motor neuron disease that causes the gradual deterioration of nerve cells in the spinal cord and brain. It impacts voluntary limb control and can result in breathing impairment. ALS diagnosis is often challenging due to its symptoms overlapping with other medical conditions and many tests must be performed to rule out other conditions, as easily identifiable biomarkers are still lacking. In this study, we explore T1-weighted (T1w) brain Magnetic Resonance Imaging (MRI), a non-invasive neuroimaging approach which has shown to be a reliable biomarker in many medical fields. Nonetheless, current literature on ALS diagnosis fails to retrieve evidence on how to identify biomarkers from T1w MRI. In this paper, we leverage Artificial Intelligence (AI) methods to unveil the unexplored potential of T1w brain MRI for distinguishing ALS patients from those who have similar symptoms but different diseases (mimicking). We consider a retrospective single-center dataset of brain T1-weighted MRIs collected from 2010 to 2018 recruited from the Piemonte and Valle d'Aosta ALS register (PARALS). The collection includes 548 patients with ALS and 106 with mimicking diseases. Our goal is to develop and validate a ML diagnostic model based exclusively on T1w MRI distinguishing the two classes. First, we extract a set of radiomic features and two sets of Deep Learning (DL)-based features from MRI scans. Then, using each representation, we train 8 binary classifiers. The best results were obtained by combining DL-based features with SVM classifier, reaching an F1-score of 0.91, and a Precision of 0.88, a Recall of 0.94, and an AUC of 0.7 considering the ALS group as the positive class in the testing set.

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Out-of-Distribution Detection with Deep Nearest Neighbors

Cited by 5 publications

References 0 publications

Rule-based Out-Of-Distribution Detection

Rule-based Out-Of-Distribution Detection

Improving security of autonomous cyber-physical systems against adversarial examples

AI-based model for T1-weighted brain MRI diagnoses Amyotrophic Lateral Sclerosis

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