mlsanityData to 7 Oct 2026

NP-Match: When Neural Processes meet Semi-Supervised Learning

arXiv 2207.01066 ↗First posted 3 Jul 2022cs.LG cs.CVcited by 25

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Abstract (arXiv)

Semi-supervised learning (SSL) has been widely explored in recent years, and it is an effective way of leveraging unlabeled data to reduce the reliance on labeled data. In this work, we adjust neural processes (NPs) to the semi-supervised image classification task, resulting in a new method named NP-Match. NP-Match is suited to this task for two reasons. Firstly, NP-Match implicitly compares data points when making predictions, and as a result, the prediction of each unlabeled data point is affected by the labeled data points that are similar to it, which improves the quality of pseudo-labels. Secondly, NP-Match is able to estimate uncertainty that can be used as a tool for selecting unlabeled samples with reliable pseudo-labels. Compared with uncertainty-based SSL methods implemented with Monte Carlo (MC) dropout, NP-Match estimates uncertainty with much less computational overhead, which can save time at both the training and the testing phases. We conducted extensive experiments on four public datasets, and NP-Match outperforms state-of-the-art (SOTA) results or achieves competitive results on them, which shows the effectiveness of NP-Match and its potential for SSL.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). Builds on 46 of its 63 references that are in the map. First posted on arXiv 3 Jul 2022.

Builds on

46 of 63 references.

References · 46

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  4. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  5. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
  6. 2017On Calibration of Modern Neural Networkscited by 4,860
  7. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  8. 2014Going Deeper with Convolutionscited by 4,231
  9. 2016Wide Residual Networkscited by 3,057
  10. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,335
  11. 2019RandAugment: Practical automated data augmentation with a reduced search spacecited by 2,261
  12. 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,080
34 more
  1. 2019MixMatch: A Holistic Approach to Semi-Supervised Learningcited by 1,390
  2. 2019Unsupervised Data Augmentation for Consistency Trainingcited by 1,057
  3. 2016Temporal Ensembling for Semi-Supervised Learningcited by 1,054
  4. 2021FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labelingcited by 537
  5. 2016Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learningcited by 441
  6. 2015Deep Kernel Learningcited by 440
  7. 2018Conditional Neural Processescited by 421
  8. 2019Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentationcited by 378
  9. 2020Meta Pseudo Labelscited by 372
  10. 2019ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoringcited by 349
  11. 2019S4L: Self-Supervised Semi-Supervised Learningcited by 335
  12. 2021In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learningcited by 301
  13. 2014Learning with Pseudo-Ensemblescited by 241
  14. 2020CoMatch: Semi-supervised Learning with Contrastive Graph Regularizationcited by 167
  15. 2018Deep Co-Training for Semi-Supervised Image Recognitioncited by 145
  16. 2019Convolutional Conditional Neural Processescited by 139
  17. 2019Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processescited by 131
  18. 2020Improving model calibration with accuracy versus uncertainty optimizationcited by 123
  19. 2021SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised Classificationcited by 68
  20. 2020Syn2Real Transfer Learning for Image Deraining using Gaussian Processescited by 62
  21. 2021Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentationcited by 61
  22. 2019Sequential Neural Processescited by 55
  23. 2019The Functional Neural Processcited by 43
  24. 2020Bootstrapping Neural Processescited by 40
  25. 2021The Gaussian Neural Processcited by 34
  26. 2018Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variancecited by 33
  27. 2021All Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-trainingcited by 32
  28. 2021Uncertainty guided semi-supervised segmentation of retinal layers in OCT imagescited by 29
  29. 2018Bayesian Semi-supervised Learning with Graph Gaussian Processescited by 28
  30. 2019Graph Convolutional Gaussian Processescited by 20
  31. 2020Calibration of Model Uncertainty for Dropout Variational Inferencecited by 18
  32. 2019Recurrent Attentive Neural Process for Sequential Datacited by 15
  33. 2020Robustifying Sequential Neural Processescited by 15
  34. 2020Grasping Detection Network with Uncertainty Estimation for Confidence-Driven Semi-Supervised Domain Adaptationcited by 9

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Citing papers per year: 2022: 3; 2023: 16; 2024: 5; 2025: 1.

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  1. 2023Boosting Semi-Supervised Learning by Exploiting All Unlabeled Datacited by 26
  2. 2022The Neural Process Family: Survey, Applications and Perspectivescited by 23
  3. 2023Semi-supervised learning made simple with self-supervised clusteringcited by 20
  4. 2023Orthogonal Annotation Benefits Barely-supervised Medical Image Segmentationcited by 15
  5. 2023NPCL: Neural Processes for Uncertainty-Aware Continual Learningcited by 14
  6. 2023Beyond Unimodal: Generalising Neural Processes for Multimodal Uncertainty Estimationcited by 11
  7. 2023RelationMatch: Matching In-batch Relationships for Semi-supervised Learningcited by 8
  8. 2023OTMatch: Improving Semi-Supervised Learning with Optimal Transportcited by 6
  9. 2023The Pursuit of Human Labeling: A New Perspective on Unsupervised Learningcited by 6
  10. 2023NP-SemiSeg: When Neural Processes meet Semi-Supervised Semantic Segmentationcited by 5
  11. 2023Enhancing Sample Utilization through Sample Adaptive Augmentation in Semi-Supervised Learningcited by 5
  12. 2022IDNP: Interest Dynamics Modeling using Generative Neural Processes for Sequential Recommendationcited by 3
13 more
  1. 2022OPERA: Omni-Supervised Representation Learning with Hierarchical Supervisionscited by 3
  2. 2024Real-time Adaptation for Condition Monitoring Signal Prediction using Label-aware Neural Processescited by 3
  3. 2024ProSub: Probabilistic Open-Set Semi-Supervised Learning with Subspace-Based Out-of-Distribution Detectioncited by 3
  4. 2024FinePseudo: Improving Pseudo-Labelling through Temporal-Alignablity for Semi-Supervised Fine-Grained Action Recognitioncited by 3
  5. 2023SSVOD: Semi-Supervised Video Object Detection with Sparse Annotationscited by 2
  6. 2025Probabilistic Interactive 3D Segmentation with Hierarchical Neural Processescited by 2
  7. 2023NP-Match: Towards a New Probabilistic Model for Semi-Supervised Learningcited by 1
  8. 2024EdgeOL: Efficient in-situ Online Learning on Edge Devicescited by 1
  9. 2023Semi-Supervised Learning with Multiple Imputations on Non-Random Missing Labelscited by 0
  10. 2023Rethinking Scale Imbalance in Semi-supervised Object Detection for Aerial Imagescited by 0
  11. 2023SemiGPC: Distribution-Aware Label Refinement for Imbalanced Semi-Supervised Learning Using Gaussian Processescited by 0
  12. 2023Feature Space Renormalization for Semi-supervised Learningcited by 0
  13. 2024Exploring Probabilistic Models for Semi-supervised Learningcited by 0