Conceitos Básicos
Optimal SCOD strategy involves Bayes classifier for ID data and linear selector in 2D space.
Resumo
This paper addresses the SCOD problem, introducing POSCOD method for optimal strategy estimation. Theoretical insights are validated empirically, showcasing superior performance of linear strategy over SIRC. Non-learnability of SCOD without OOD data is established.
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Introduction
- Standard methods rely on closed-world assumption.
- Interest in deep learning models for OOD data handling.
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The SCOD Problem and its Optimal Solution
- Defines SCOD as a decision-making problem.
- Presents optimal solution involving Bayes classifier and linear selector.
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Relation to Existing OODD and SCOD Strategies
- Compares single-score and double-score strategies.
- Introduces Softmax Information Retaining Combination (SIRC).
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SCOD Problem is not PAC Learnable
- Extends PAC learnability concept to address SCOD problem.
- Demonstrates non-learnability without OOD data.
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Plugin Estimate of the Optimal SCOD Strategy
- Introduces POSCOD method for learning plugin estimate.
- Simplifies learning process using unlabeled mixture of ID and OOD data.
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Experiments
- Validates theoretical results empirically.
- Shows superior performance of linear strategy with empirical likelihood ratio estimation.
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Conclusions
- Highlights key contributions and implications of the study.
Estatísticas
ベイズ分類器と2D空間の線形セレクターが最適なSCOD戦略を示す。
現在のOOD検出方法やSIRCに比べて、線形戦略が優れたパフォーマンスを示す。
Citações
"Optimal prediction strategy comprises Bayes classifier for ID data."
"Linear selector outperforms existing ODD methods."