Understanding Label Bias in Single Positive Multi-Label Learning

• URL: http://arxiv.org/abs/2305.15584v1
• Date: Wed, 24 May 2023 21:41:08 GMT
• Title: Understanding Label Bias in Single Positive Multi-Label Learning
• Authors: Julio Arroyo and Pietro Perona and Elijah Cole
• Abstract summary: It is possible to train effective multi-labels using only one positive label per image. Standard benchmarks for SPML are derived from traditional multi-label classification datasets. This work introduces protocols for studying label bias in SPML and provides new empirical results.
• Score: 20.09309971112425
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Annotating data for multi-label classification is prohibitively expensive because every category of interest must be confirmed to be present or absent. Recent work on single positive multi-label (SPML) learning shows that it is possible to train effective multi-label classifiers using only one positive label per image. However, the standard benchmarks for SPML are derived from traditional multi-label classification datasets by retaining one positive label for each training example (chosen uniformly at random) and discarding all other labels. In realistic settings it is not likely that positive labels are chosen uniformly at random. This work introduces protocols for studying label bias in SPML and provides new empirical results.

Related papers

• Dist-PU: Positive-Unlabeled Learning from a Label Distribution Perspective [89.5370481649529]
  We propose a label distribution perspective for PU learning in this paper. Motivated by this, we propose to pursue the label distribution consistency between predicted and ground-truth label distributions. Experiments on three benchmark datasets validate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2022-12-06T07:38:29Z)
• An Effective Approach for Multi-label Classification with Missing Labels [8.470008570115146]
  We propose a pseudo-label based approach to reduce the cost of annotation without bringing additional complexity to the classification networks. By designing a novel loss function, we are able to relax the requirement that each instance must contain at least one positive label. We show that our method can handle the imbalance between positive labels and negative labels, while still outperforming existing missing-label learning approaches.
  arXiv  Detail & Related papers  (2022-10-24T23:13:57Z)
• 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)
• Instance-Dependent Partial Label Learning [69.49681837908511]
  Partial label learning is a typical weakly supervised learning problem. Most existing approaches assume that the incorrect labels in each training example are randomly picked as the candidate labels. In this paper, we consider instance-dependent and assume that each example is associated with a latent label distribution constituted by the real number of each label.
  arXiv  Detail & Related papers  (2021-10-25T12:50:26Z)
• Multi-Label Learning from Single Positive Labels [37.17676289125165]
  Predicting all applicable labels for a given image is known as multi-label classification. We show that it is possible to approach the performance of fully labeled classifiers despite training with significantly fewer confirmed labels.
  arXiv  Detail & Related papers  (2021-06-17T17:58:04Z)
• A Study on the Autoregressive and non-Autoregressive Multi-label Learning [77.11075863067131]
  We propose a self-attention based variational encoder-model to extract the label-label and label-feature dependencies jointly. Our model can therefore be used to predict all labels in parallel while still including both label-label and label-feature dependencies.
  arXiv  Detail & Related papers  (2020-12-03T05:41:44Z)
• Unsupervised Person Re-identification via Multi-label Classification [55.65870468861157]
  This paper formulates unsupervised person ReID as a multi-label classification task to progressively seek true labels. Our method starts by assigning each person image with a single-class label, then evolves to multi-label classification by leveraging the updated ReID model for label prediction. To boost the ReID model training efficiency in multi-label classification, we propose the memory-based multi-label classification loss (MMCL)
  arXiv  Detail & Related papers  (2020-04-20T12:13:43Z)
• Partial Multi-label Learning with Label and Feature Collaboration [21.294791188490056]
  Partial multi-label learning (PML) models the scenario where each training instance is annotated with a set of candidate labels. To achieve a credible predictor on PML data, we propose PML-LFC (Partial Multi-label Learning with Label and Feature Collaboration) PML-LFC estimates the confidence values of relevant labels for each instance using the similarity from both the label and feature spaces.
  arXiv  Detail & Related papers  (2020-03-17T08:34:45Z)

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.