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arxiv: 2405.12969 · v3 · submitted 2024-05-21 · 💻 cs.LG

EchoAlign: Bridging Generative and Discriminative Learning under Noisy Labels

Yuxiang Zheng , Zhongyi Han , Yilong Yin This is my paper

Pith reviewed 2026-05-24 01:13 UTC · model grok-4.3

classification 💻 cs.LG
keywords noisy labelsgenerative modelsdiscriminative learninginstance-dependent noisefeature alignmentsample selectionrobust learning
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The pith

EchoAlign modifies instance features with generative models to align them to noisy labels and selects reliable originals by similarity.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes a framework called EchoAlign that handles noisy labels by changing the instances to match the given labels instead of correcting the labels. It employs controllable generative models to tweak features while keeping structural details such as shape and edges intact. A selection step then keeps only those original instances whose features remain close to their modified versions. This generative-discriminative loop is shown to support training when noise reaches high levels and when instance features are ambiguous. Tests on three benchmark datasets indicate stronger results than prior methods, particularly with instance-dependent noise.

Core claim

EchoAlign bridges generative and discriminative learning under noisy labels by treating noisy labels as supervision targets and modifying the corresponding instances to align with them. EchoMod uses controllable generative models to adjust instance features while preserving key instance-level structural cues such as shape and edges and avoiding excessive distortion. EchoSelect mitigates distribution shifts by retaining a reliable subset of original instances guided by feature similarity between original and modified samples.

What carries the argument

EchoAlign framework with EchoMod component for generative feature adjustment to noisy labels and EchoSelect component for similarity-based retention of reliable instances.

If this is right

  • Outperforms state-of-the-art methods in most evaluated settings on three benchmark datasets.
  • Under 30 percent instance-dependent noise, retains nearly twice as many correctly labeled samples as competing approaches.
  • Maintains 99 percent selection accuracy under 30 percent instance-dependent noise.
  • Enables robust learning in highly noisy settings without relying on label correction.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same modification-plus-similarity pattern might apply directly to other modalities such as text or audio where controllable generators exist.
  • Avoiding explicit label correction could retain partial signal from noisy labels that are only locally incorrect.
  • Similarity-based selection could be combined with existing confidence or consistency filters to further improve retention rates.

Load-bearing premise

Controllable generative models can reliably adjust instance features to align with noisy labels while preserving key structural cues without excessive distortion, and feature similarity between original and modified samples can accurately identify reliable instances without introducing new biases.

What would settle it

A replication on the same three benchmarks under 30 percent instance-dependent noise in which EchoSelect fails to retain nearly twice as many correct samples as competitors or drops below 99 percent selection accuracy would falsify the performance claims.

Figures

Figures reproduced from arXiv: 2405.12969 by Yilong Yin, Yuxiang Zheng, Zhongyi Han.

Figure 1
Figure 1. Figure 1: Instance modification effectively aligns instances with their labels, while label [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: (Top) Main challenge 1: Characteristic Shift. (Bottom) Main challenge 2: [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: A graphical causal model, revealing a data generative process with instance [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The feature similarity between the original and modified instances is a valuable [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: (a) illustrates the mutual information between the labels of 50,000 original [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Figures (a), (b), and (c) respectively illustrate the differences in training and [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: The framework of EchoAlign. model’s ability to learn meaningful patterns. EchoMod addresses this by transforming data instances to be consistent with their noisy labels. This controlled modification helps the model extract relevant information even when labels contain noise. Mechanism EchoMod leverages a pre-trained controllable generative model (e.g., a controllable diffusion-based model) to modify data i… view at source ↗
Figure 8
Figure 8. Figure 8: (a) Comparison of the effect of the threshold ( [PITH_FULL_IMAGE:figures/full_fig_p021_8.png] view at source ↗
read the original abstract

Noisy labels severely hinder the accuracy and generalization of machine learning models, especially when ambiguous instance features make reliable annotation difficult. Existing approaches, including transition-matrix-based label correction, struggle to capture complex relationships between instances and noisy labels, limiting their effectiveness in such settings. We present EchoAlign, a framework that bridges generative and discriminative learning under noisy labels. Instead of correcting labels, EchoAlign treats noisy labels as supervision targets and modifies the corresponding instances to align with them. The framework has two components: EchoMod uses controllable generative models to adjust instance features while preserving key instance-level structural cues, such as shape and edges, and avoiding excessive distortion; EchoSelect mitigates distribution shifts by retaining a reliable subset of original instances, guided by feature similarity between original and modified samples. This generative-discriminative interplay enables robust learning in highly noisy settings. Experiments on three benchmark datasets show that EchoAlign outperforms state-of-the-art methods in most evaluated settings. Under 30% instance-dependent noise, EchoSelect retains nearly twice as many correctly labeled samples as competing approaches while maintaining 99% selection accuracy, demonstrating the robustness and effectiveness of EchoAlign.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes EchoAlign, a framework that bridges generative and discriminative learning for noisy label problems. Rather than correcting labels, EchoMod uses controllable generative models to modify instance features to align with noisy labels while preserving structural cues like shape and edges. EchoSelect then retains a reliable subset of original instances based on feature similarity between original and modified samples to mitigate distribution shift. Experiments on three benchmark datasets show outperformance over state-of-the-art methods in most settings; under 30% instance-dependent noise, EchoSelect retains nearly twice as many correctly labeled samples as competitors while achieving 99% selection accuracy.

Significance. If the empirical results hold, the work offers a novel alternative to transition-matrix and label-correction approaches by aligning instances to noisy labels instead. The concrete metrics on sample retention and selection accuracy under instance-dependent noise provide evidence of robustness in challenging regimes. The manuscript is credited for presenting a fully empirical framework with standard benchmark comparisons rather than relying on untestable theoretical reductions.

major comments (2)
  1. [§5] §5 (experimental results): the reported 99% selection accuracy and 2× retention under 30% instance-dependent noise are load-bearing for the central claim, yet the manuscript provides no ablation on the similarity threshold or generative-model hyperparameters used to generate the modified samples; without these controls it is unclear whether the gains are robust to implementation choices.
  2. [method section] EchoMod description (method section): the claim that controllable generative models adjust features 'while preserving key instance-level structural cues and avoiding excessive distortion' is central to the framework, but no quantitative distortion metric or preservation guarantee is stated or measured; this leaves the weakest assumption untested in the reported experiments.
minor comments (2)
  1. [abstract and §5] The abstract and experimental tables should include error bars or standard deviations across runs to support the outperformance statements.
  2. [EchoSelect description] Notation for the similarity function in EchoSelect is introduced without an explicit equation; adding one would improve clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the positive recommendation of minor revision and the detailed comments. We address each major comment below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [§5] §5 (experimental results): the reported 99% selection accuracy and 2× retention under 30% instance-dependent noise are load-bearing for the central claim, yet the manuscript provides no ablation on the similarity threshold or generative-model hyperparameters used to generate the modified samples; without these controls it is unclear whether the gains are robust to implementation choices.

    Authors: We agree that the reported metrics would be strengthened by explicit ablations. In the revised manuscript we will add results varying the similarity threshold over a range of values and testing multiple generative-model hyperparameter settings, confirming that the 99% selection accuracy and retention gains remain stable. revision: yes

  2. Referee: [method section] EchoMod description (method section): the claim that controllable generative models adjust features 'while preserving key instance-level structural cues and avoiding excessive distortion' is central to the framework, but no quantitative distortion metric or preservation guarantee is stated or measured; this leaves the weakest assumption untested in the reported experiments.

    Authors: The manuscript currently supports the preservation claim via qualitative examples and downstream task performance. We acknowledge that a quantitative metric would provide stronger validation. We will add a quantitative distortion measure (e.g., LPIPS between original and modified instances) in the revised experiments section. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper presents an empirical ML framework (EchoMod + EchoSelect) for noisy-label learning. No derivation chain, equations, or first-principles claims appear in the abstract or described sections. Performance claims rest on benchmark experiments rather than any reduction of outputs to fitted inputs or self-citations. The method is self-contained as a practical pipeline whose validity is tested externally via standard datasets and metrics; no load-bearing step reduces by construction to its own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only abstract available; no free parameters, axioms, or invented entities can be identified from the provided text.

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discussion (0)

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Forward citations

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Reference graph

Works this paper leans on

70 extracted references · 70 canonical work pages · cited by 1 Pith paper · 4 internal anchors

  1. [1]

    GPT-4 Technical Report

    J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  2. [2]

    Arazo, D

    E. Arazo, D. Ortego, P. Albert, N. O’Connor, and K. McGuinness. Unsupervised label noise modeling and loss correction. In International conference on machine learning, pages 312--321. PMLR, 2019

    work page 2019

  3. [3]

    Arpit, S

    D. Arpit, S. Jastrzebski, N. Ballas, D. Krueger, E. Bengio, M. S. Kanwal, T. Maharaj, A. Fischer, A. C. Courville, Y. Bengio, and S. Lacoste - Julien. A closer look at memorization in deep networks. In ICML, pages 233--242, 2017

    work page 2017

  4. [4]

    Y. Bai, E. Yang, B. Han, Y. Yang, J. Li, Y. Mao, G. Niu, and T. Liu. Understanding and improving early stopping for learning with noisy labels. In NeurIPS, pages 24392--24403, 2021

    work page 2021

  5. [5]

    Y. Bai, Z. Han, E. Yang, J. Yu, B. Han, D. Wang, and T. Liu. Subclass-dominant label noise: A counterexample for the success of early stopping. In Thirty-seventh Conference on Neural Information Processing Systems, 2023

    work page 2023

  6. [6]

    Berthon, B

    A. Berthon, B. Han, G. Niu, T. Liu, and M. Sugiyama. Confidence scores make instance-dependent label-noise learning possible. In ICML, Proceedings of Machine Learning Research, pages 825--836, 2021

    work page 2021

  7. [7]

    J. Bose, R. P. Monti, and A. Grover. Controllable generative modeling via causal reasoning. Transactions on Machine Learning Research, 2022

    work page 2022

  8. [8]

    H. Chen, J. Wang, A. Shah, R. Tao, H. Wei, X. Xie, M. Sugiyama, and B. Raj. Understanding and mitigating the label noise in pre-training on downstream tasks. arXiv preprint arXiv:2309.17002, 2023 a

    work page arXiv 2023

  9. [9]

    H. Chen, B. Raj, X. Xie, and J. Wang. On catastrophic inheritance of large foundation models. arXiv preprint arXiv:2402.01909, 2024

    work page arXiv 2024

  10. [10]

    J. Chen, R. Zhang, T. Yu, R. Sharma, Z. Xu, T. Sun, and C. Chen. Label-retrieval-augmented diffusion models for learning from noisy labels. ArXiv, abs/2305.19518, 2023 b . doi:10.48550/arXiv.2305.19518

    work page doi:10.48550/arxiv.2305.19518 2023

  11. [11]

    T. Chen, Y. Liu, Z. Wang, J. Yuan, Q. You, H. Yang, and M. Zhou. Improving in-context learning in diffusion models with visual context-modulated prompts. arXiv preprint arXiv:2312.01408, 2023 c

    work page arXiv 2023

  12. [12]

    Cheng, T

    J. Cheng, T. Liu, K. Ramamohanarao, and D. Tao. Learning with bounded instance and label-dependent label noise. In International conference on machine learning, pages 1789--1799. PMLR, 2020

    work page 2020

  13. [13]

    Dosovitskiy, L

    A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In ICLR, 2021

    work page 2021

  14. [14]

    H. Du, H. Yuan, Z. Huang, P. Zhao, and X. Zhou. Sequential recommendation with diffusion models. ArXiv, abs/2304.04541, 2023. doi:10.48550/arXiv.2304.04541

    work page doi:10.48550/arxiv.2304.04541 2023

  15. [15]

    Franceschi, M

    J.-Y. Franceschi, M. Gartrell, L. D. Santos, T. Issenhuth, E. de B'ezenac, M. Chen, and A. Rakotomamonjy. Unifying gans and score-based diffusion as generative particle models. ArXiv, abs/2305.16150, 2023. doi:10.48550/arXiv.2305.16150

    work page doi:10.48550/arxiv.2305.16150 2023

  16. [16]

    Goldberger and E

    J. Goldberger and E. Ben-Reuven. Training deep neural-networks using a noise adaptation layer. In International conference on learning representations, 2016

    work page 2016

  17. [17]

    K. Gu, X. Masotto, V. Bachani, B. Lakshminarayanan, J. Nikodem, and D. Yin. An instance-dependent simulation framework for learning with label noise. Machine Learning, 112 0 (6): 0 1871--1896, 2023

    work page 2023

  18. [18]

    B. Han, Q. Yao, X. Yu, G. Niu, M. Xu, W. Hu, I. Tsang, and M. Sugiyama. Co-teaching: Robust training of deep neural networks with extremely noisy labels. In NeurIPS, pages 8527--8537, 2018

    work page 2018

  19. [19]

    B. Han, G. Niu, X. Yu, Q. Yao, M. Xu, I. Tsang, and M. Sugiyama. SIGUA : Forgetting may make learning with noisy labels more robust. In International Conference on Machine Learning, pages 4006--4016, 2020

    work page 2020

  20. [20]

    Han, X.-J

    Z. Han, X.-J. Gui, H. Sun, Y. Yin, and S. Li. Towards accurate and robust domain adaptation under multiple noisy environments. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45 0 (5): 0 6460--6479, 2022 a

    work page 2022

  21. [21]

    Z. Han, H. Sun, and Y. Yin. Learning transferable parameters for unsupervised domain adaptation. IEEE Transactions on Image Processing, 31: 0 6424--6439, 2022 b

    work page 2022

  22. [22]

    K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In CVPR, pages 770--778, 2016

    work page 2016

  23. [23]

    T. Kim, J. Ko, S. Cho, J. Choi, and S. Yun. FINE samples for learning with noisy labels. In NeurIPS, pages 24137--24149, 2021

    work page 2021

  24. [24]

    Kingma, T

    D. Kingma, T. Salimans, B. Poole, and J. Ho. Variational diffusion models. Advances in neural information processing systems, 34: 0 21696--21707, 2021

    work page 2021

  25. [25]

    Krizhevsky, G

    A. Krizhevsky, G. Hinton, et al. Learning multiple layers of features from tiny images. Technical report, 2009

    work page 2009

  26. [26]

    J. Li, R. Socher, and S. C. H. Hoi. Dividemix: Learning with noisy labels as semi-supervised learning. In ICLR, 2020

    work page 2020

  27. [27]

    W. Li, L. Wang, W. Li, E. Agustsson, and L. Van Gool. Webvision database: Visual learning and understanding from web data. arXiv preprint arXiv:1708.02862, 2017

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  28. [28]

    S. Liu, J. Niles - Weed, N. Razavian, and C. Fernandez - Granda. Early-learning regularization prevents memorization of noisy labels. In NeurIPS, pages 20331--20342, 2020

    work page 2020

  29. [29]

    Liu and D

    T. Liu and D. Tao. Classification with noisy labels by importance reweighting. IEEE Transactions on pattern analysis and machine intelligence, 38 0 (3): 0 447--461, 2015

    work page 2015

  30. [30]

    Y. Liu, H. Cheng, and K. Zhang. Identifiability of label noise transition matrix. In International Conference on Machine Learning, pages 21475--21496. PMLR, 2023

    work page 2023

  31. [31]

    Y. Lu, Y. Bo, and W. He. Noise attention learning: Enhancing noise robustness by gradient scaling. In NeurIPS, 2022

    work page 2022

  32. [32]

    X. Ma, H. Huang, Y. Wang, S. Romano, S. Erfani, and J. Bailey. Normalized loss functions for deep learning with noisy labels. In ICML, 2020

    work page 2020

  33. [33]

    A. K. Menon, B. Van Rooyen, and N. Natarajan. Learning from binary labels with instance-dependent noise. Machine Learning, 107: 0 1561--1595, 2018

    work page 2018

  34. [34]

    A. K. Menon, A. S. Rawat, S. J. Reddi, and S. Kumar. Can gradient clipping mitigate label noise? In International Conference on Learning Representations, 2019

    work page 2019

  35. [35]

    Natarajan, I

    N. Natarajan, I. S. Dhillon, P. K. Ravikumar, and A. Tewari. Learning with noisy labels. Advances in neural information processing systems, 26, 2013

    work page 2013

  36. [36]

    L. G. Neuberg. Causality: models, reasoning, and inference, by judea pearl, cambridge university press, 2000. Econometric Theory, 19 0 (4): 0 675--685, 2003

    work page 2000

  37. [37]

    D. T. Nguyen, C. K. Mummadi, T. Ngo, T. H. P. Nguyen, L. Beggel, and T. Brox. SELF: learning to filter noisy labels with self-ensembling. In ICLR, 2020

    work page 2020

  38. [38]

    Peters, D

    J. Peters, D. Janzing, and B. Sch \"o lkopf. Elements of causal inference: foundations and learning algorithms. The MIT Press, 2017

    work page 2017

  39. [39]

    Radford, J

    A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748--8763. PMLR, 2021

    work page 2021

  40. [40]

    S. Reed, H. Lee, D. Anguelov, C. Szegedy, D. Erhan, and A. Rabinovich. Training deep neural networks on noisy labels with bootstrapping. arXiv preprint arXiv:1412.6596, 2014

    work page internal anchor Pith review Pith/arXiv arXiv 2014

  41. [41]

    C. Scott. A rate of convergence for mixture proportion estimation, with application to learning from noisy labels. In Artificial Intelligence and Statistics, pages 838--846. PMLR, 2015

    work page 2015

  42. [42]

    Scott, G

    C. Scott, G. Blanchard, and G. Handy. Classification with asymmetric label noise: Consistency and maximal denoising. In Conference on learning theory, pages 489--511. PMLR, 2013

    work page 2013

  43. [43]

    Stiennon, L

    N. Stiennon, L. Ouyang, J. Wu, D. Ziegler, R. Lowe, C. Voss, A. Radford, D. Amodei, and P. F. Christiano. Learning to summarize with human feedback. In NeurIPS, pages 3008--3021, 2020

    work page 2020

  44. [44]

    Tan and Q

    M. Tan and Q. V. Le. Efficientnet: Rethinking model scaling for convolutional neural networks. In ICML, pages 6105--6114, 2019

    work page 2019

  45. [45]

    Tanaka, D

    D. Tanaka, D. Ikami, T. Yamasaki, and K. Aizawa. Joint optimization framework for learning with noisy labels. In CVPR, pages 5552--5560, 2018

    work page 2018

  46. [46]

    G. Team, R. Anil, S. Borgeaud, Y. Wu, J.-B. Alayrac, J. Yu, R. Soricut, J. Schalkwyk, A. M. Dai, A. Hauth, et al. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805, 2023

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  47. [47]

    X. Wang, S. Wang, J. Wang, H. Shi, and T. Mei. Co-mining: Deep face recognition with noisy labels. In Proceedings of the IEEE/CVF international conference on computer vision, pages 9358--9367, 2019

    work page 2019

  48. [48]

    Y. Wang, Z. Liu, L. Yang, and P. S. Yu. Conditional denoising diffusion for sequential recommendation. ArXiv, abs/2304.11433, 2023. doi:10.48550/arXiv.2304.11433

    work page doi:10.48550/arxiv.2304.11433 2023

  49. [49]

    H. Wei, H. Zhuang, R. Xie, L. Feng, G. Niu, B. An, and Y. Li. Mitigating memorization of noisy labels by clipping the model prediction. In International Conference on Machine Learning. PMLR, 2023

    work page 2023

  50. [50]

    J. Wei, Z. Zhu, H. Cheng, T. Liu, G. Niu, and Y. Liu. Learning with noisy labels revisited: A study using real-world human annotations. In International Conference on Learning Representations, 2022

    work page 2022

  51. [51]

    Welinder, S

    P. Welinder, S. Branson, S. J. Belongie, and P. Perona. The multidimensional wisdom of crowds. In NeurIPS, pages 2424--2432, 2010

    work page 2010

  52. [52]

    P. Wu, S. Zheng, M. Goswami, D. N. Metaxas, and C. Chen. A topological filter for learning with label noise. In NeurIPS, pages 21382--21393, 2020

    work page 2020

  53. [53]

    X. Xia, T. Liu, N. Wang, B. Han, C. Gong, G. Niu, and M. Sugiyama. Are anchor points really indispensable in label-noise learning? In NeurIPS, pages 6835--6846, 2019

    work page 2019

  54. [54]

    X. Xia, T. Liu, B. Han, N. Wang, M. Gong, H. Liu, G. Niu, D. Tao, and M. Sugiyama. Part-dependent label noise: Towards instance-dependent label noise. Advances in Neural Information Processing Systems, 33: 0 7597--7610, 2020

    work page 2020

  55. [55]

    X. Xia, T. Liu, B. Han, C. Gong, N. Wang, Z. Ge, and Y. Chang. Robust early-learning: Hindering the memorization of noisy labels. In ICLR, 2021

    work page 2021

  56. [56]

    X. Xia, B. Han, Y. Zhan, J. Yu, M. Gong, C. Gong, and T. Liu. Combating noisy labels with sample selection by mining high-discrepancy examples. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1833--1843, 2023 a

    work page 2023

  57. [57]

    X. Xia, P. Lu, C. Gong, B. Han, J. Yu, and T. Liu. Regularly truncated m-estimators for learning with noisy labels. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 b

    work page 2023

  58. [58]

    T. Xiao, T. Xia, Y. Yang, C. Huang, and X. Wang. Learning from massive noisy labeled data for image classification. In CVPR, pages 2691--2699, 2015

    work page 2015

  59. [59]

    S. Yang, E. Yang, B. Han, Y. Liu, M. Xu, G. Niu, and T. Liu. Estimating instance-dependent bayes-label transition matrix using a deep neural network. In ICML, pages 25302--25312, 2022

    work page 2022

  60. [60]

    Y. Yao, T. Liu, M. Gong, B. Han, G. Niu, and K. Zhang. Instance-dependent label-noise learning under a structural causal model. Advances in Neural Information Processing Systems, 34: 0 4409--4420, 2021

    work page 2021

  61. [61]

    Y. Yao, M. Gong, Y. Du, J. Yu, B. Han, K. Zhang, and T. Liu. Which is better for learning with noisy labels: the semi-supervised method or modeling label noise? In International Conference on Machine Learning, pages 39660--39673. PMLR, 2023 a

    work page 2023

  62. [62]

    Y. Yao, T. Liu, M. Gong, B. Han, G. Niu, and K. Zhang. Causality encourages the identifiability of instance-dependent label noise. In Machine Learning for Causal Inference, pages 247--264. Springer, 2023 b

    work page 2023

  63. [63]

    X. Yu, T. Liu, M. Gong, and D. Tao. Learning with biased complementary labels. In ECCV, pages 69--85, 2018

    work page 2018

  64. [64]

    X. Yu, B. Han, J. Yao, G. Niu, I. Tsang, and M. Sugiyama. How does disagreement help generalization against label corruption? In International Conference on Machine Learning, pages 7164--7173. PMLR, 2019

    work page 2019

  65. [65]

    Zhang, M

    H. Zhang, M. Ciss \' e , Y. N. Dauphin, and D. Lopez - Paz. mixup: Beyond empirical risk minimization. In ICLR, 2018

    work page 2018

  66. [66]

    Zhang, V

    J. Zhang, V. S. Sheng, T. Li, and X. Wu. Improving crowdsourced label quality using noise correction. IEEE transactions on neural networks and learning systems, 29 0 (5): 0 1675--1688, 2017

    work page 2017

  67. [67]

    Zhang, A

    L. Zhang, A. Rao, and M. Agrawala. Adding conditional control to text-to-image diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3836--3847, 2023

    work page 2023

  68. [68]

    S. Zhao, D. Chen, Y.-C. Chen, J. Bao, S. Hao, L. Yuan, and K.-Y. K. Wong. Uni-controlnet: All-in-one control to text-to-image diffusion models. Advances in Neural Information Processing Systems, 2023

    work page 2023

  69. [69]

    X. Zhou, X. Liu, D. Zhai, J. Jiang, and X. Ji. Asymmetric loss functions for noise-tolerant learning: Theory and applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023

    work page 2023

  70. [70]

    Zhuang, Y

    Y. Zhuang, Y. Yu, L. Kong, X. Chen, and C. Zhang. Dygen: Learning from noisy labels via dynamics-enhanced generative modeling. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023. doi:10.1145/3580305.3599318

    work page doi:10.1145/3580305.3599318 2023