A Survey on Open Set Recognition

Mahdavi, Atefeh; Carvalho, Marco

doi:10.48550/arxiv.2109.00893

Cited by 4 publications

(3 citation statements)

References 108 publications

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“…Further downstream tasks and user/analyst acceptance (trust) are therefore improved when receiving well-calibrated posterior confidence scores. We further desire not to give highly confident predictions at test/inference time to "unwanted" examples, such as out-of-domain/out-ofclass or adversarial examples, and thus extensions with Open Set Recognition [12,17] and adversarial detection techniques [2] are also of importance to include in real-world scenarios.…”

Section: Believabilitymentioning

confidence: 99%

Confident AI

Davis¹

2022

Preprint

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In this paper, we propose "Confident AI" as a means to designing Artificial Intelligence (AI) and Machine Learning (ML) systems with both algorithm and user confidence in model predictions and reported results. The 4 basic tenets of Confident AI are Repeatability, Believability, Sufficiency, and Adaptability. Each of the tenets is used to explore fundamental issues in current AI/ML systems and together provide an overall approach to Confident AI.

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Section: Believabilitymentioning

confidence: 99%

Confident AI

Davis¹

2022

Preprint

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“…Elucidating the interplay of dataset sizes, model capacities, and distributional shifts, especially relaxation of sample preparation protocols (e.g., using knife cuts instead of progressive sanding, or sanding to a coarser grit [thus involving fewer steps and less time]) and the closed-world assumption (Scheirer et al 2013), is likely to be an important challenge in the realization of general fielddeployable CVWID systems. We expect the exploration of these ideas (e.g., Mahdavi and Carvalho 2021;Vaze et al 2021;Yang et al 2021) to be a fertile area for future work.…”

Section: Deployment Gap Of Cross-validation and Field Testingmentioning

confidence: 99%

Towards sustainable North American wood product value chains, part 2: computer vision identification of ring-porous hardwoods

et al. 2022

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Wood identification is vitally important for ensuring the legality of North American hardwood value chains. Computer vision wood identification (CVWID) systems can identify wood without necessitating costly and time-consuming offsite visual inspections by highly trained wood anatomists. Previous work by Ravindran et al. presented macroscopic CVWID models for identification of North American diffuse porous hardwoods from 22 wood anatomically informed classes using the open-source XyloTron platform. This manuscript expands on that work by training and evaluating complementary 17-class XyloTron CVWID models for the identification of North American ring porous hardwoods – woods that display spatial heterogeneity in earlywood and latewood pore size and distribution and other radial growth-rate related features. Deep learning models trained using 4045 images from 452 ring-porous wood specimens from four xylaria demonstrated 98% five-fold cross-validation accuracy. A field model trained on all the training data and subsequently tested on 198 specimens drawn from two additional xylaria achieved top-1 and top-2 predictions of 91.4% and 100%, respectively, and images devoid of earlywood, latewood, or broad rays did not greatly reduce the prediction accuracy. This study advocates for continued cooperation between wood anatomy and machine learning experts for implementing and evaluating field-operational CVWID systems.

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“…In the following years other works considered one-class classifiers for detection of new classes [25,26]. Although these methods in general can be used for OS classification, the different problem setting results in a comparatively low detection performance [27,28]. To the best of the authors' knowledge, the literature provides no work applying classifiers inherently made for the OS problem to event classification in active DNs.…”

Section: Thematically-related Workmentioning

confidence: 99%

Unsupervised detection and open-set classification of fast-ramped flexibility activation events

Müller,

Heinrich,

Heussen

et al. 2021

Preprint

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The continuous electrification of the mobility and heating sectors adds much-needed flexibility to the power system. However, flexibility utilization also introduces new challenges to distribution system operators (DSOs), who need mechanisms to supervise flexibility activations and monitor their effect on distribution network operation. Flexibility activations can be broadly categorized to those originating from electricity markets and those initiated by the DSO to avoid constraint violations. Simultaneous electricity market driven flexibility activations may cause voltage quality or temporary overloading issues, and the failure of flexibility activations initiated by the DSO might leave critical grid states unresolved. This work proposes a novel data processing pipeline for automated real-time identification of fast-ramped flexibility activation events. Its practical value is twofold: i) potentially critical flexibility activations originating from electricity markets can be detected by the DSO at an early stage, and ii) successful activation of DSO-requested flexibility can be verified by the operator. In both cases the increased awareness would allow the DSO to take counteractions to avoid potentially critical grid situations. The proposed pipeline combines techniques from unsupervised detection and open-set classification. For both building blocks feasibility is systematically evaluated and proofed on real load and flexibility activation data.

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A Survey on Open Set Recognition

Cited by 4 publications

References 108 publications

Confident AI

Confident AI

Towards sustainable North American wood product value chains, part 2: computer vision identification of ring-porous hardwoods

Unsupervised detection and open-set classification of fast-ramped flexibility activation events

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