Misclassification Detection via Class Augmentation
Despite the impressive performance in various pattern recognition tasks, deep neural networks (DNNs) are typically overconfident in their predictions, making it difficult to determine whether a test example is misclassified. In this paper, we propose a simple yet effective method of class augmentation (classAug) to address the challenge of misclassification detection in DNNs. Specifically, we increase the number of classes during training by assigning new classes to the samples generated using between-class interpolation. In spite of the simplicity, extensive experiments demonstrate that the misclassification detection performance of DNNs can be significantly improved by seeing more generated pseudo-classes during training. Additionally, we observe that DNNs trained with classAug are more robust on out-of-distribution examples and better calibrated. Finally, as a general regularization strategy, classAug can also enhance the original classification accuracy and few-shot learning performance.
Code (0)
등록된 구현이 없습니다.
Tasks
Few-Shot LearningSimilar Papers 제목 키워드 기반
Counterexample-Guided Data Augmentation
We present a novel framework for augmenting data sets for machine learning based on counterexamples. Counterexamples are misclassified examples that have important properties for retraining and improving the model. Key c…
Autonomous DrivingData Augmentationobject-detectionObject DetectionAdversarial Patch Attack for Ship Detection via Localized Augmentation
Current ship detection techniques based on remote sensing imagery primarily rely on the object detection capabilities of deep neural networks (DNNs). However, DNNs are vulnerable to adversarial patch attacks, which can l…
Object DetectionA Data-Driven Measure of Relative Uncertainty for Misclassification Detection
Misclassification detection is an important problem in machine learning, as it allows for the identification of instances where the model's predictions are unreliable. However, conventional uncertainty measures such as S…
image-classificationImage ClassificationUncertainty Estimation of Transformer Predictions for Misclassification Detection
Uncertainty estimation (UE) of model predictions is a crucial step for a variety of tasks such as active learning, misclassification detection, adversarial attack detection, out-of-distribution detection, etc. Most of th…
Active LearningAdversarial AttackAdversarial Attack DetectionClassification+8EviNet: Evidential Reasoning Network for Resilient Graph Learning in the Open and Noisy Environments
Graph learning has been crucial to many real-world tasks, but they are often studied with a closed-world assumption, with all possible labels of data known a priori. To enable effective graph learning in an open and nois…
Graph LearningLogical ReasoningOut-of-Distribution Detection