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Deng-Bao Wang

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8 papers

2 author rows

IJCAI Conference 2025 Conference Paper

Wrapped Partial Label Dimensionality Reduction via Dependence Maximization

Xiang-Ru Yu
Deng-Bao Wang
Min-Ling Zhang

Partial label learning induces classifier from data with ambiguous supervision, where each instance is associated with a set of candidate labels but only one of which is valid. As a classic data preprocessing strategy, dimensionality reduction contributes to enhance the generalization capabilities of learning algorithms. Due to the ambiguity of supervision, existing works on partial label dimensionality reduction are confined to two separate stages: dimensionality reduction and partial label disambiguation. However, the decoupling of dimensionality reduction from partial label disambiguation can lead to severe performance degradation. In this paper, we present a novel approach called Wrapped Partial Label Dimensionality Reduction (WPLDR) to address this challenge. Specifically, WPLDR integrates the dimensionality reduction and partial label disambiguation within a unified framework, employing alternating optimization to concurrently perform dimensionality reduction and partial label disambiguation. WPLDR maximizes the interdependence between features in the embedded space and confidence-based label information, while simultaneously ensuring the manifold consistency between the embedded feature space and label space. Extensive experiments over a broad range of synthetic and real-world partial label data sets validate that the performance of well-established partial label learning algorithms can be significantly improved by the proposed WPLDR.

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ICML Conference 2024 Conference Paper

Calibration Bottleneck: Over-compressed Representations are Less Calibratable

Deng-Bao Wang
Min-Ling Zhang

Although deep neural networks have achieved remarkable success, they often exhibit a significant deficiency in reliable uncertainty calibration. This paper focus on model calibratability, which assesses how amenable a model is to be well recalibrated post-hoc. We find that the widely used weight decay regularizer detrimentally affects model calibratability, subsequently leading to a decline in final calibration performance after post-hoc calibration. To identify the underlying causes leading to poor calibratability, we delve into the calibratability of intermediate features across the hidden layers. We observe a U-shaped trend in the calibratability of intermediate features from the bottom to the top layers, which indicates that over-compression of the top representation layers significantly hinders model calibratability. Based on the observations, this paper introduces a weak classifier hypothesis, i. e. , given a weak classification head that has not been over-trained, the representation module can be better learned to produce more calibratable features. Consequently, we propose a progressively layer-peeled training (PLP) method to exploit this hypothesis, thereby enhancing model calibratability. Our comparative experiments show the effectiveness of our method, which improves model calibration and also yields competitive predictive performance.

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AAAI Conference 2024 Conference Paper

Distilling Reliable Knowledge for Instance-Dependent Partial Label Learning

Dong-Dong Wu
Deng-Bao Wang
Min-Ling Zhang

Partial label learning (PLL) refers to the classification task where each training instance is ambiguously annotated with a set of candidate labels. Despite substantial advancements in tackling this challenge, limited attention has been devoted to a more specific and realistic setting, denoted as instance-dependent partial label learning (IDPLL). Within this contex, the assignment of partial labels depends on the distinct features of individual instances, rather than being random. In this paper, we initiate an exploration into a self-distillation framework for this problem, driven by the proven effectiveness and stability of this framework. Nonetheless, a crucial shortfall is identified: the foundational assumption central to IDPLL, involving what we term as partial label knowledge stipulating that candidate labels should exhibit superior confidence compared to non-candidates, is not fully upheld within the distillation process. To address this challenge, we introduce DIRK, a novel distillation approach that leverages a rectification process to DIstill Reliable Knowledge, while concurrently preserves informative fine-grained label confidence. In addition, to harness the rectified confidence to its fullest potential, we propose a knowledge-based representation refinement module, seamlessly integrated into the DIRK framework. This module effectively transmits the essence of similarity knowledge from the label space to the feature space, thereby amplifying representation learning and subsequently engendering marked improvements in model performance. Experiments and analysis on multiple datasets validate the rationality and superiority of our proposed approach.

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AAAI Conference 2023 Conference Paper

Partial-Label Regression

Xin Cheng
Deng-Bao Wang
Lei Feng
Min-Ling Zhang
Bo An

Partial-label learning is a popular weakly supervised learning setting that allows each training example to be annotated with a set of candidate labels. Previous studies on partial-label learning only focused on the classification setting where candidate labels are all discrete, which cannot handle continuous labels with real values. In this paper, we provide the first attempt to investigate partial-label regression, where each training example is annotated with a set of real-valued candidate labels. To solve this problem, we first propose a simple baseline method that takes the average loss incurred by candidate labels as the predictive loss. The drawback of this method lies in that the loss incurred by the true label may be overwhelmed by other false labels. To overcome this drawback, we propose an identification method that takes the least loss incurred by candidate labels as the predictive loss. We further improve it by proposing a progressive identification method to differentiate candidate labels using progressively updated weights for incurred losses. We prove that the latter two methods are model-consistent and provide convergence analysis showing the optimal parametric convergence rate. Our proposed methods are theoretically grounded and can be compatible with any models, optimizers, and losses. Experiments validate the effectiveness of our proposed methods.

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ICML Conference 2022 Conference Paper

Revisiting Consistency Regularization for Deep Partial Label Learning

Dong-Dong Wu
Deng-Bao Wang
Min-Ling Zhang

Partial label learning (PLL), which refers to the classification task where each training instance is ambiguously annotated with a set of candidate labels, has been recently studied in deep learning paradigm. Despite advances in recent deep PLL literature, existing methods (e. g. , methods based on self-training or contrastive learning) are confronted with either ineffectiveness or inefficiency. In this paper, we revisit a simple idea namely consistency regularization, which has been shown effective in traditional PLL literature, to guide the training of deep models. Towards this goal, a new regularized training framework, which performs supervised learning on non-candidate labels and employs consistency regularization on candidate labels, is proposed for PLL. We instantiate the regularization term by matching the outputs of multiple augmentations of an instance to a conformal label distribution, which can be adaptively inferred by the closed-form solution. Experiments on benchmark datasets demonstrate the superiority of the proposed method compared with other state-of-the-art methods.

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IJCAI Conference 2021 Conference Paper

Learning from Complementary Labels via Partial-Output Consistency Regularization

Deng-Bao Wang
Lei Feng
Min-Ling Zhang

In complementary-label learning (CLL), a multi-class classifier is learned from training instances each associated with complementary labels, which specify the classes that the instance does not belong to. Previous studies focus on unbiased risk estimator or surrogate loss while neglect the importance of regularization in training phase. In this paper, we give the first attempt to leverage regularization techniques for CLL. By decoupling a label vector into complementary labels and partial unknown labels, we simultaneously inhibit the outputs of complementary labels with a complementary loss and penalize the sensitivity of the classifier on the partial outputs of these unknown classes by consistency regularization. Then we unify the complementary loss and consistency loss together by a specially designed dynamic weighting factor. We conduct a series of experiments showing that the proposed method achieves highly competitive performance in CLL.

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AAAI Conference 2021 Conference Paper

Learning from Noisy Labels with Complementary Loss Functions

Deng-Bao Wang
Yong Wen
Lujia Pan
Min-Ling Zhang

Recent researches reveal that deep neural networks are sensitive to label noises hence leading to poor generalization performance in some tasks. Although different robust loss functions have been proposed to remedy this issue, they suffer from an underfitting problem, thus are not sufficient to learn accurate models. On the other hand, the commonly used Cross Entropy (CE) loss, which shows high performance in standard supervised learning (with clean supervision), is nonrobust to label noise. In this paper, we propose a general framework to learn robust deep neural networks with complementary loss functions. In our framework, CE and robust loss play complementary roles in a joint learning objective as per their learning sufficiency and robustness properties respectively. Specifically, we find that by exploiting the memorization effect of neural networks, we can easily filter out a proportion of hard samples and generate reliable pseudo labels for easy samples, and thus reduce the label noise to a quite low level. Then, we simply learn with CE on pseudo supervision and robust loss on original noisy supervision. In this procedure, CE can guarantee the sufficiency of optimization while the robust loss can be regarded as the supplement. Experimental results on benchmark classification datasets indicate that the proposed method helps achieve robust and sufficient deep neural network training simultaneously.

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NeurIPS Conference 2021 Conference Paper

Rethinking Calibration of Deep Neural Networks: Do Not Be Afraid of Overconfidence

Deng-Bao Wang
Lei Feng
Min-Ling Zhang

Capturing accurate uncertainty quantification of the prediction from deep neural networks is important in many real-world decision-making applications. A reliable predictor is expected to be accurate when it is confident about its predictions and indicate high uncertainty when it is likely to be inaccurate. However, modern neural networks have been found to be poorly calibrated, primarily in the direction of overconfidence. In recent years, there is a surge of research on model calibration by leveraging implicit or explicit regularization techniques during training, which obtain well calibration by avoiding overconfident outputs. In our study, we empirically found that despite the predictions obtained from these regularized models are better calibrated, they suffer from not being as calibratable, namely, it is harder to further calibrate their predictions with post-hoc calibration methods like temperature scaling and histogram binning. We conduct a series of empirical studies showing that overconfidence may not hurt final calibration performance if post-hoc calibration is allowed, rather, the penalty of confident outputs will compress the room of potential improvements in post-hoc calibration phase. Our experimental findings point out a new direction to improve calibration of DNNs by considering main training and post-hoc calibration as a unified framework.

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