Dual-Label Learning With Irregularly Present Labels

• URL: http://arxiv.org/abs/2410.14380v2
• Date: Mon, 21 Oct 2024 01:28:32 GMT
• Title: Dual-Label Learning With Irregularly Present Labels
• Authors: Mingqian Li, Qiao Han, Yiteng Zhai, Ruifeng Li, Yao Yang, Hongyang Chen,
• Abstract summary: This work focuses on the two-label learning task, and proposes a novel training and inference framework, Dual-Label Learning (DLL) Our method makes consistently better predictions than baseline approaches by up to a 10% gain in F1-score or MAPE. Remarkably, our method provided with data at a label missing rate as high as 60% can achieve similar or even better results than baseline approaches at a label missing rate of only 10%.
• Score: 14.817794592309637
• License:
• Abstract: In multi-task learning, we often encounter the case when the presence of labels across samples exhibits irregular patterns: samples can be fully labeled, partially labeled or unlabeled. Taking drug analysis as an example, multiple toxicity properties of a drug molecule may not be concurrently available due to experimental limitations. It triggers a demand for a new training and inference mechanism that could accommodate irregularly present labels and maximize the utility of any available label information. In this work, we focus on the two-label learning task, and propose a novel training and inference framework, Dual-Label Learning (DLL). The DLL framework formulates the problem into a dual-function system, in which the two functions should simultaneously satisfy standard supervision, structural duality and probabilistic duality. DLL features a dual-tower model architecture that explicitly captures the information exchange between labels, aimed at maximizing the utility of partially available labels in understanding label correlation. During training, label imputation for missing labels is conducted as part of the forward propagation process, while during inference, labels are regarded as unknowns of a bivariate system of equations and are solved jointly. Theoretical analysis guarantees the feasibility of DLL, and extensive experiments are conducted to verify that by explicitly modeling label correlation and maximizing the utility of available labels, our method makes consistently better predictions than baseline approaches by up to a 10% gain in F1-score or MAPE. Remarkably, our method provided with data at a label missing rate as high as 60% can achieve similar or even better results than baseline approaches at a label missing rate of only 10%.

Related papers

• Appeal: Allow Mislabeled Samples the Chance to be Rectified in Partial Label Learning [55.4510979153023]
  In partial label learning (PLL), each instance is associated with a set of candidate labels among which only one is ground-truth. To help these mislabeled samples "appeal," we propose the first appeal-based framework.
  arXiv  Detail & Related papers  (2023-12-18T09:09:52Z)
• Robust Representation Learning for Unreliable Partial Label Learning [86.909511808373]
  Partial Label Learning (PLL) is a type of weakly supervised learning where each training instance is assigned a set of candidate labels, but only one label is the ground-truth. This is known as Unreliable Partial Label Learning (UPLL) that introduces an additional complexity due to the inherent unreliability and ambiguity of partial labels. We propose the Unreliability-Robust Representation Learning framework (URRL) that leverages unreliability-robust contrastive learning to help the model fortify against unreliable partial labels effectively.
  arXiv  Detail & Related papers  (2023-08-31T13:37:28Z)
• Deep Partial Multi-Label Learning with Graph Disambiguation [27.908565535292723]
  We propose a novel deep Partial multi-Label model with grAph-disambIguatioN (PLAIN) Specifically, we introduce the instance-level and label-level similarities to recover label confidences. At each training epoch, labels are propagated on the instance and label graphs to produce relatively accurate pseudo-labels.
  arXiv  Detail & Related papers  (2023-05-10T04:02:08Z)
• Complementary to Multiple Labels: A Correlation-Aware Correction Approach [65.59584909436259]
  We show theoretically how the estimated transition matrix in multi-class CLL could be distorted in multi-labeled cases. We propose a two-step method to estimate the transition matrix from candidate labels.
  arXiv  Detail & Related papers  (2023-02-25T04:48:48Z)
• 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)
• 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)
• 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)
• Enhancing Label Correlation Feedback in Multi-Label Text Classification via Multi-Task Learning [6.1538971100140145]
  We introduce a novel approach with multi-task learning to enhance label correlation feedback. We propose two auxiliary label co-occurrence prediction tasks to enhance label correlation learning.
  arXiv  Detail & Related papers  (2021-06-06T12:26:14Z)
• Generalized Label Enhancement with Sample Correlations [24.582764493585362]
  We propose two novel label enhancement methods, i.e., Label Enhancement with Sample Correlations (LESC) and generalized Label Enhancement with Sample Correlations (gLESC) Benefitting from the sample correlations, the proposed methods can boost the performance of label enhancement.
  arXiv  Detail & Related papers  (2020-04-07T03:32:36Z)

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.