Drainage: A Unifying Framework for Addressing Class Uncertainty

• URL: http://arxiv.org/abs/2512.03182v1
• Date: Tue, 02 Dec 2025 19:31:01 GMT
• Title: Drainage: A Unifying Framework for Addressing Class Uncertainty
• Authors: Yasser Taha, Grégoire Montavon, Nils Körber,
• Abstract summary: We propose a unified framework based on the concept of a "drainage node" which we add at the output of the network.<n>This mechanism provides a natural escape route for highly ambiguous, anomalous, or noisy samples.<n>Our formulation achieves an accuracy increase of up to 9% over existing approaches in the high-noise regime.
• Score: 5.204620543649613
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern deep learning faces significant challenges with noisy labels, class ambiguity, as well as the need to robustly reject out-of-distribution or corrupted samples. In this work, we propose a unified framework based on the concept of a "drainage node'' which we add at the output of the network. The node serves to reallocate probability mass toward uncertainty, while preserving desirable properties such as end-to-end training and differentiability. This mechanism provides a natural escape route for highly ambiguous, anomalous, or noisy samples, particularly relevant for instance-dependent and asymmetric label noise. In systematic experiments involving the addition of varying proportions of instance-dependent noise or asymmetric noise to CIFAR-10/100 labels, our drainage formulation achieves an accuracy increase of up to 9\% over existing approaches in the high-noise regime. Our results on real-world datasets, such as mini-WebVision, mini-ImageNet and Clothing-1M, match or surpass existing state-of-the-art methods. Qualitative analysis reveals a denoising effect, where the drainage neuron consistently absorbs corrupt, mislabeled, or outlier data, leading to more stable decision boundaries. Furthermore, our drainage formulation enables applications well beyond classification, with immediate benefits for web-scale, semi-supervised dataset cleaning, and open-set applications.

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