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näkemys - Prediction Error-based Classification (PEC)
Prediction Error-based Classification for Class-Incremental Learning: A Novel Approach
Prediction Error-based Classification (PEC) introduces a novel approach for class-incremental learning by utilizing prediction errors to generate class scores, outperforming other methods in single-pass-through-data scenarios.
Prediction Error-based Classification for Class-Incremental Learning: A Novel Approach for Efficient Learning
Prediction Error-based Classification (PEC) offers a novel and efficient approach for class-incremental learning, outperforming other methods in single-pass-through-data scenarios.
Prediction Error-based Classification for Class-Incremental Learning: A Novel Approach for Efficient Learning
Prediction Error-based Classification (PEC) offers a novel and efficient approach for class-incremental learning, outperforming other methods in single-pass-through-data scenarios.
Klassifizierung basierend auf Vorherschungsfehlern für Klassen-inkrementelles Lernen
PEC bietet eine effiziente Alternative für generative Klassifizierung in Class-Incremental Learning.
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