Learning with Multiple Complementary Labels

ICML, pp. 3072-3081, 2020.

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We propose a novel problem setting called learning with multiple complementary labels, which is a generation of complementary-label learning

Abstract:

A complementary label (CL) simply indicates an incorrect class of an example, but learning with CLs results in multi-class classifiers that can predict the correct class. Unfortunately, the problem setting only allows a single CL for each example, which notably limits its potential since our labelers may easily identify multiple CLs (MCLs...More

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Introduction
  • Ordinary machine learning tasks generally require massive data with accurate supervision information, while it is expensive and time-consuming to collect the data with high-quality labels.
  • To alleviate this problem, the researchers have studied various weakly supervised learning frameworks (Zhou, 2018), including semi-supervised learning (Chapelle et al, 2006; Li & Liang, 2019; Miyato et al, 2018; Niu et al, 2013; Zhu & Goldberg, 2009), positive-.
  • Multiple complementary labels (MCLs) would be more widespread than a single CL
Highlights
  • Ordinary machine learning tasks generally require massive data with accurate supervision information, while it is expensive and time-consuming to collect the data with high-quality labels
  • In order to solve the above problems, we further propose an unbiased risk estimator (Section 4.2) for learning with multiple complementary labels, which processes each set of multiple complementary labels as a whole
  • We propose a novel problem setting called learning with multiple complementary labels (MCLs), which is a generation of complementary-label learning (Ishida et al, 2017; 2019; Yu et al, 2018)
  • We find that the supervision information that multiple complementary labels hold is conceptually diluted after decomposition
  • We further propose an unbiased risk estimator for learning with multiple complementary labels, which processes each set of multiple complementary labels as whole
  • Our risk estimator does not rely on specific models or loss functions, we show that bounded loss is generally better than unbounded loss in our empirical risk estimator
Methods
  • The authors conduct extensive experiments to evaluate the performance of the proposed approaches including the two wrappers, the unbiased risk estimator with various loss functions and the two upper-bound surrogate loss functions.

    Datasets.
  • The authors use four base models including linear model, MLP model (d-500-k), ResNet (34 layers) (He et al, 2016), and DenseNet (22 layers) (Huang et al, 2017).
  • The detailed descriptions of these datasets with the corresponding base models are provided in Appendix E.1.
  • The authors first randomly sample s from ppsq, and uniformly and randomly sample a complementary label set Ys with size s
Conclusion
  • The authors propose a novel problem setting called learning with multiple complementary labels (MCLs), which is a generation of complementary-label learning (Ishida et al, 2017; 2019; Yu et al, 2018)
  • To solve this learning problem, the authors first design two wrappers that enable them to use arbitrary complementary-label learning approaches for learning with MCLs. the authors find that the supervision information that MCLs hold is conceptually diluted after decomposition.
Summary
  • Introduction:

    Ordinary machine learning tasks generally require massive data with accurate supervision information, while it is expensive and time-consuming to collect the data with high-quality labels.
  • To alleviate this problem, the researchers have studied various weakly supervised learning frameworks (Zhou, 2018), including semi-supervised learning (Chapelle et al, 2006; Li & Liang, 2019; Miyato et al, 2018; Niu et al, 2013; Zhu & Goldberg, 2009), positive-.
  • Multiple complementary labels (MCLs) would be more widespread than a single CL
  • Methods:

    The authors conduct extensive experiments to evaluate the performance of the proposed approaches including the two wrappers, the unbiased risk estimator with various loss functions and the two upper-bound surrogate loss functions.

    Datasets.
  • The authors use four base models including linear model, MLP model (d-500-k), ResNet (34 layers) (He et al, 2016), and DenseNet (22 layers) (Huang et al, 2017).
  • The detailed descriptions of these datasets with the corresponding base models are provided in Appendix E.1.
  • The authors first randomly sample s from ppsq, and uniformly and randomly sample a complementary label set Ys with size s
  • Conclusion:

    The authors propose a novel problem setting called learning with multiple complementary labels (MCLs), which is a generation of complementary-label learning (Ishida et al, 2017; 2019; Yu et al, 2018)
  • To solve this learning problem, the authors first design two wrappers that enable them to use arbitrary complementary-label learning approaches for learning with MCLs. the authors find that the supervision information that MCLs hold is conceptually diluted after decomposition.
Tables
  • Table1: Supervision information for a set of MCLs (with size s). Setting #TP #FP Supervision Purity
  • Table2: Classification accuracy (meanstd) of each algorithm on the four UCI datasets using a linear model for 5 trials. The best performance among all the approaches is highlighted in boldface. In addition, ‚{ ̋ indicates whether the performance of our approach (the best of EXP and LOG) is statistically superior/inferior to the comparing algorithm on each dataset (paired t-test at 0.05 significance level)
  • Table3: Classification accuracy (meanstd) of each algorithm on the four benchmark datasets using a linear model for 5 trials. The best performance among all the approaches is highlighted in boldface. In addition, ‚{ ̋ indicates whether the performance of our approach (the best of EXP and LOG) is statistically superior/inferior to the comparing algorithm on each dataset (paired t-test at 0.05 significance level)
  • Table4: Classification accuracy (meanstd) of each algorithm on the five benchmark datasets using neural networks for 5 trials. The best performance among all the approaches is highlighted in boldface. In addition, ‚{ ̋ indicates whether the performance of our approach (the best of EXP and LOG) is statistically superior/inferior to the comparing algorithm on each dataset (paired t-test at 0.05 significance level)
Download tables as Excel
Related work
  • In this section, we introduce some notations and briefly review the formulations of multi-class classification and complementary-label learning.

    2.1. Multi-Class Classification

    Suppose the feature space is X P Rd with d dimensions and the label space is Y “ t1, 2, . . . , ku with k classes, the instance x P X with its class label y P Y is sampled from an unknown probability distribution with density ppx, yq. Ordinary multi-class classification aims to induce a learning function f pxq : Rd Ñ Rk that minimizes the classification risk: Rpf q “ Eppx,yq“Lf pxq, y‰, (1)

    where Lf pxq, yis a multi-class loss function. The predicted label is given as y “ argmaxyPY fypxq, where fypxq is the y-th coordinate of f pxq.

    2.2. Complementary-Label Learning

    Suppose the dataset for complementary-label learning is denoted by tpxi, ysiquni“1, where ysi P Y is a complementary label of xi, and each complementarily labeled example is sampled from pspx, ysq. Ishida et al (2017; 2019) assumed that pspx, ysq is expressed as: pspx, ysq “
Funding
  • This research was supported by the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG-RP-2019-0013), National Satellite of Excellence in Trustworthy Software Systems (Award No: NSOE-TSS2019-01), and NTU
  • BH was partially supported by HKBU Tier-1 Start-up Grant and HKBU CSD Start-up Grant
  • GN and MS were supported by JST AIP Acceleration Research Grant Number JPMJCR20U3, Japan
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