In this work we attempt to compare between learning using soft and hard labels to train K-nearest neighbor classifiers. We propose a new technique to generate ...

Results reveal that learning with soft labels is more robust against label errors opposed to learning with crisp labels. The proposed technique to find soft ...

In this work we attempt to compare between learning using soft and hard labels to train K-nearest neighbor classifiers. We propose a new technique to generate ...

Results reveal that learning with soft labels is more robust against label errors opposed to learning with crisp labels. The proposed technique to find soft ...

The use of soft labels in classification for non-time series data sets has been studied and has shown robust prediction against label noise [7, 21]. Several ...

Also, we will answer the question if classifiers trained on soft labels are more resilient to label noise than those trained on hard labels. 1 Introduction. In ...

Experiments demonstrate that ASK-Def provides additional robustness for kNN-based deep classifiers compared to con- ventional adversarial training. ASK-Atk ...

May 30, 2024 · In this paper, however, we investigate whether biased soft labels are still effective. Here, bias refers to the discrepancy between the soft labels and the ...

In this paper we propose an approach for Fuzzy-Input Fuzzy-Output classification in which the classifier can learn with soft-labeled data and can also produce ...

A curated list of most recent papers & codes in Learning with Noisy Labels. Some recent works about group-distributional robustness, label distribution shifts, ...