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Yun-Peng Liu

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3 papers

2 author rows

IJCAI Conference 2020 Conference Paper

Label Distribution for Learning with Noisy Labels

Yun-Peng Liu
Ning Xu
Yu Zhang
Xin Geng

The performances of deep neural networks (DNNs) crucially rely on the quality of labeling. In some situations, labels are easily corrupted, and therefore some labels become noisy labels. Thus, designing algorithms that deal with noisy labels is of great importance for learning robust DNNs. However, it is difficult to distinguish between clean labels and noisy labels, which becomes the bottleneck of many methods. To address the problem, this paper proposes a novel method named Label Distribution based Confidence Estimation (LDCE). LDCE estimates the confidence of the observed labels based on label distribution. Then, the boundary between clean labels and noisy labels becomes clear according to confidence scores. To verify the effectiveness of the method, LDCE is combined with the existing learning algorithm to train robust DNNs. Experiments on both synthetic and real-world datasets substantiate the superiority of the proposed algorithm against state-of-the-art methods.

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AAAI Conference 2020 Conference Paper

Partial Multi-Label Learning with Label Distribution

Ning Xu
Yun-Peng Liu
Xin Geng

Partial multi-label learning (PML) aims to learn from training examples each associated with a set of candidate labels, among which only a subset are valid for the training example. The common strategy to induce predictive model is trying to disambiguate the candidate label set, such as identifying the ground-truth label via utilizing the conﬁdence of each candidate label or estimating the noisy labels in the candidate label sets. Nonetheless, these strategies ignore considering the essential label distribution corresponding to each instance since the label distribution is not explicitly available in the training set. In this paper, a new partial multi-label learning strategy named PML-LD is proposed to learn from partial multi-label examples via label enhancement. Speciﬁcally, label distributions are recovered by leveraging the topological information of the feature space and the correlations among the labels. After that, a multi-class predictive model is learned by ﬁtting a regularized multi-output regressor with the recovered label distributions. Experimental results on synthetic as well as real-world datasets clearly validate the effectiveness of PML- LD for solving PML problems.

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ICML Conference 2020 Conference Paper

Variational Label Enhancement

Ning Xu 0009
Jun Shu
Yun-Peng Liu
Xin Geng 0001

Label distribution covers a certain number of labels, representing the degree to which each label describes the instance. When dealing with label ambiguity, label distribution could describe the supervised information in a fine-grained way. Unfortunately, many training sets only contain simple logical labels rather than label distributions due to the difficulty of obtaining label distributions directly. To solve this problem, we consider the label distributions as the latent vectors and infer them from the logical labels in the training datasets by using variational inference. After that, we induce a predictive model to train the label distribution data by employing the multi-output regression technique. The recovery experiment on thirteen real-world LDL datasets and the predictive experiment on ten multi-label learning datasets validate the advantage of our approach over the state-of-the-art approaches.

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