Boosting Single Positive Multi-label Classification with Generalized Robust Loss

• URL: http://arxiv.org/abs/2405.03501v1
• Date: Mon, 6 May 2024 14:13:38 GMT
• Title: Boosting Single Positive Multi-label Classification with Generalized Robust Loss
• Authors: Yanxi Chen, Chunxiao Li, Xinyang Dai, Jinhuan Li, Weiyu Sun, Yiming Wang, Renyuan Zhang, Tinghe Zhang, Bo Wang,
• Abstract summary: Multi-label learning (MLL) requires comprehensive multi-semantic annotations that is hard to fully obtain. In this paper, we investigate Single Positive Multi-label Learning (SPML), where each image is associated with merely one positive label. Existing SPML methods only focus on designing losses using mechanisms such as hard pseudo-labeling and robust losses.
• Score: 12.942661993684881
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-label learning (MLL) requires comprehensive multi-semantic annotations that is hard to fully obtain, thus often resulting in missing labels scenarios. In this paper, we investigate Single Positive Multi-label Learning (SPML), where each image is associated with merely one positive label. Existing SPML methods only focus on designing losses using mechanisms such as hard pseudo-labeling and robust losses, mostly leading to unacceptable false negatives. To address this issue, we first propose a generalized loss framework based on expected risk minimization to provide soft pseudo labels, and point out that the former losses can be seamlessly converted into our framework. In particular, we design a novel robust loss based on our framework, which enjoys flexible coordination between false positives and false negatives, and can additionally deal with the imbalance between positive and negative samples. Extensive experiments show that our approach can significantly improve SPML performance and outperform the vast majority of state-of-the-art methods on all the four benchmarks.

Related papers

• When Measures are Unreliable: Imperceptible Adversarial Perturbations toward Top-$k$ Multi-Label Learning [83.8758881342346]
  A novel loss function is devised to generate adversarial perturbations that could achieve both visual and measure imperceptibility. Experiments on large-scale benchmark datasets demonstrate the superiority of our proposed method in attacking the top-$k$ multi-label systems.
  arXiv  Detail & Related papers  (2023-07-27T13:18:47Z)
• Class-Distribution-Aware Pseudo Labeling for Semi-Supervised Multi-Label Learning [97.88458953075205]
  Pseudo-labeling has emerged as a popular and effective approach for utilizing unlabeled data. This paper proposes a novel solution called Class-Aware Pseudo-Labeling (CAP) that performs pseudo-labeling in a class-aware manner.
  arXiv  Detail & Related papers  (2023-05-04T12:52:18Z)
• Easy Learning from Label Proportions [17.71834385754893]
  Easyllp is a flexible and simple-to-implement debiasing approach based on aggregate labels. Our technique allows us to accurately estimate the expected loss of an arbitrary model at an individual level.
  arXiv  Detail & Related papers  (2023-02-06T20:41:38Z)
• SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised Learning [101.86916775218403]
  This paper revisits the popular pseudo-labeling methods via a unified sample weighting formulation. We propose SoftMatch to overcome the trade-off by maintaining both high quantity and high quality of pseudo-labels during training. In experiments, SoftMatch shows substantial improvements across a wide variety of benchmarks, including image, text, and imbalanced classification.
  arXiv  Detail & Related papers  (2023-01-26T03:53:25Z)
• Controller-Guided Partial Label Consistency Regularization with Unlabeled Data [49.24911720809604]
  We propose a controller-guided consistency regularization at both the label-level and representation-level. We dynamically adjust the confidence thresholds so that the number of samples of each class participating in consistency regularization remains roughly equal to alleviate the problem of class-imbalance.
  arXiv  Detail & Related papers  (2022-10-20T12:15:13Z)
• One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancement [71.9401831465908]
  We investigate single-positive multi-label learning (SPMLL) where each example is annotated with only one relevant label. A novel method named proposed, i.e., Single-positive MultI-label learning with Label Enhancement, is proposed. Experiments on benchmark datasets validate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2022-06-01T14:26:30Z)
• Acknowledging the Unknown for Multi-label Learning with Single Positive Labels [65.5889334964149]
  Traditionally, all unannotated labels are assumed as negative labels in single positive multi-label learning (SPML) We propose entropy-maximization (EM) loss to maximize the entropy of predicted probabilities for all unannotated labels. Considering the positive-negative label imbalance of unannotated labels, we propose asymmetric pseudo-labeling (APL) with asymmetric-tolerance strategies and a self-paced procedure to provide more precise supervision.
  arXiv  Detail & Related papers  (2022-03-30T11:43:59Z)
• Simple and Robust Loss Design for Multi-Label Learning with Missing Labels [14.7306301893944]
  We propose two simple yet effective methods via robust loss design based on an observation a model can identify missing labels during training. The first is a novel robust loss for negatives, namely the Hill loss, which re-weights negatives in the shape of a hill to alleviate the effect of false negatives. The second is a self-paced loss correction (SPLC) method, which uses a loss derived from the maximum likelihood criterion under an approximate distribution of missing labels.
  arXiv  Detail & Related papers  (2021-12-13T11:39:19Z)
• Multi-Label Sampling based on Local Label Imbalance [7.355362369511579]
  Class imbalance is an inherent characteristic of multi-label data that hinders most multi-label learning methods. Existing multi-label sampling approaches alleviate the global imbalance of multi-label datasets. It is actually the imbalance level within the local neighbourhood of minority class examples that plays a key role in performance degradation.
  arXiv  Detail & Related papers  (2020-05-07T04:14:23Z)

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.