Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and Adaptivity

• URL: http://arxiv.org/abs/2112.14869v4
• Date: Wed, 28 Jun 2023 04:53:43 GMT
• Title: Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and Adaptivity
• Authors: Dixian Zhu, Yiming Ying and Tianbao Yang
• Abstract summary: We study a family of loss functions named label-distributionally robust (LDR) losses for multi-class classification. Our contributions include both consistency and robustness by establishing top-$k$ consistency of LDR losses for multi-class classification. We propose a new adaptive LDR loss that automatically adapts the individualized temperature parameter to the noise degree of class label of each instance.
• Score: 55.29408396918968
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study a family of loss functions named label-distributionally robust (LDR) losses for multi-class classification that are formulated from distributionally robust optimization (DRO) perspective, where the uncertainty in the given label information are modeled and captured by taking the worse case of distributional weights. The benefits of this perspective are several fold: (i) it provides a unified framework to explain the classical cross-entropy (CE) loss and SVM loss and their variants, (ii) it includes a special family corresponding to the temperature-scaled CE loss, which is widely adopted but poorly understood; (iii) it allows us to achieve adaptivity to the uncertainty degree of label information at an instance level. Our contributions include: (1) we study both consistency and robustness by establishing top-$k$ ($\forall k\geq 1$) consistency of LDR losses for multi-class classification, and a negative result that a top-$1$ consistent and symmetric robust loss cannot achieve top-$k$ consistency simultaneously for all $k\geq 2$; (2) we propose a new adaptive LDR loss that automatically adapts the individualized temperature parameter to the noise degree of class label of each instance; (3) we demonstrate stable and competitive performance for the proposed adaptive LDR loss on 7 benchmark datasets under 6 noisy label and 1 clean settings against 13 loss functions, and on one real-world noisy dataset. The code is open-sourced at \url{https://github.com/Optimization-AI/ICML2023_LDR}.

Related papers

• Energy Score-based Pseudo-Label Filtering and Adaptive Loss for Imbalanced Semi-supervised SAR target recognition [1.2035771704626825]
  Existing semi-supervised SAR ATR algorithms show low recognition accuracy in the case of class imbalance. This work offers a non-balanced semi-supervised SAR target recognition approach using dynamic energy scores and adaptive loss.
  arXiv  Detail & Related papers  (2024-11-06T14:45:16Z)
• An extended asymmetric sigmoid with Perceptron (SIGTRON) for imbalanced linear classification [0.0]
  This article presents a new parameterized sigmoid called SIGTRON, and its companion convex model called SIGTRON-imbalanced classification (SIC) model. In contrast to the conventional $pi$-weighted cost-sensitive learning model, the SIC model does not have an external $pi$-weight on the loss function. We show that the proposed SIC model is more adaptive to variations of the dataset.
  arXiv  Detail & Related papers  (2023-12-26T13:14:17Z)
• Generating Unbiased Pseudo-labels via a Theoretically Guaranteed Chebyshev Constraint to Unify Semi-supervised Classification and Regression [57.17120203327993]
  threshold-to-pseudo label process (T2L) in classification uses confidence to determine the quality of label. In nature, regression also requires unbiased methods to generate high-quality labels. We propose a theoretically guaranteed constraint for generating unbiased labels based on Chebyshev's inequality.
  arXiv  Detail & Related papers  (2023-11-03T08:39:35Z)
• Uncertainty-Aware Source-Free Adaptive Image Super-Resolution with Wavelet Augmentation Transformer [60.31021888394358]
  Unsupervised Domain Adaptation (UDA) can effectively address domain gap issues in real-world image Super-Resolution (SR) We propose a SOurce-free Domain Adaptation framework for image SR (SODA-SR) to address this issue, i.e., adapt a source-trained model to a target domain with only unlabeled target data.
  arXiv  Detail & Related papers  (2023-03-31T03:14:44Z)
• The KFIoU Loss for Rotated Object Detection [115.334070064346]
  In this paper, we argue that one effective alternative is to devise an approximate loss who can achieve trend-level alignment with SkewIoU loss. Specifically, we model the objects as Gaussian distribution and adopt Kalman filter to inherently mimic the mechanism of SkewIoU. The resulting new loss called KFIoU is easier to implement and works better compared with exact SkewIoU.
  arXiv  Detail & Related papers  (2022-01-29T10:54:57Z)
• S3: Supervised Self-supervised Learning under Label Noise [53.02249460567745]
  In this paper we address the problem of classification in the presence of label noise. In the heart of our method is a sample selection mechanism that relies on the consistency between the annotated label of a sample and the distribution of the labels in its neighborhood in the feature space. Our method significantly surpasses previous methods on both CIFARCIFAR100 with artificial noise and real-world noisy datasets such as WebVision and ANIMAL-10N.
  arXiv  Detail & Related papers  (2021-11-22T15:49:20Z)
• Robust Classification via Support Vector Machines [1.7520660701924717]
  We propose two robust classifiers under data uncertainty. The first is called Single Perturbation SVM (SP-SVM) and provides a constructive method by allowing a controlled perturbation to one feature of the data. The second method is called Extreme Empirical Loss SVM (EEL-SVM) and is based on a new empirical loss estimate, namely, the Extreme Empirical Loss (EEL)
  arXiv  Detail & Related papers  (2021-04-27T20:20:12Z)
• Label-Imbalanced and Group-Sensitive Classification under Overparameterization [32.923780772605596]
  Label-imbalanced and group-sensitive classification seeks to appropriately modify standard training algorithms to optimize relevant metrics. We show that a logit-adjusted loss modification to standard empirical risk minimization might be ineffective in general. We show that our results extend naturally to binary classification with sensitive groups, thus treating the two common types of imbalances (label/group) in a unifying way.
  arXiv  Detail & Related papers  (2021-03-02T08:09:43Z)
• Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification [137.9939571408506]
  We estimate and exploit the credibility of the assigned pseudo-label of each sample to alleviate the influence of noisy labels. Our uncertainty-guided optimization brings significant improvement and achieves the state-of-the-art performance on benchmark datasets.
  arXiv  Detail & Related papers  (2020-12-16T04:09:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.