mlsanityData to 7 Oct 2026

Meta-DM: Applications of Diffusion Models on Few-Shot Learning

arXiv 2305.08092 ↗First posted 14 May 2023cs.LG cs.CVcited by 4

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

In the field of few-shot learning (FSL), extensive research has focused on improving network structures and training strategies. However, the role of data processing modules has not been fully explored. Therefore, in this paper, we propose Meta-DM, a generalized data processing module for FSL problems based on diffusion models. Meta-DM is a simple yet effective module that can be easily integrated with existing FSL methods, leading to significant performance improvements in both supervised and unsupervised settings. We provide a theoretical analysis of Meta-DM and evaluate its performance on several algorithms. Our experiments show that combining Meta-DM with certain methods achieves state-of-the-art results.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 36 of its 43 references that are in the map. First posted on arXiv 14 May 2023.

Builds on

36 of 43 references.

Closest ancestors · 3

Same direction, newest first

  1. 2021High-Resolution Image Synthesis with Latent Diffusion Models20 Dec
  2. 2021Diffusion Models Beat GANs on Image Synthesis11 May
  3. 2020Denoising Diffusion Probabilistic Models19 Jun

Other references · 33

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  4. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  5. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  6. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  7. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,893
  8. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,466
  9. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  10. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  11. 2020Exploring Simple Siamese Representation Learningcited by 2,853
  12. 2021BEiT: BERT Pre-Training of Image Transformerscited by 2,360
21 more
  1. 2019RandAugment: Practical automated data augmentation with a reduced search spacecited by 2,261
  2. 2016Matching Networks for One Shot Learningcited by 2,246
  3. 2017Learning to Compare: Relation Network for Few-Shot Learningcited by 1,139
  4. 2019Meta-Learning with Differentiable Convex Optimizationcited by 461
  5. 2018Meta-Learning for Semi-Supervised Few-Shot Classificationcited by 456
  6. 2020Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?cited by 435
  7. 2019Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examplescited by 312
  8. 2020Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learningcited by 179
  9. 2022Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Differencecited by 123
  10. 2016Learning feed-forward one-shot learnerscited by 93
  11. 2017Good Semi-supervised Learning that Requires a Bad GANcited by 85
  12. 2018Unsupervised Learning via Meta-Learningcited by 81
  13. 2018Unsupervised Meta-Learning For Few-Shot Image Classificationcited by 74
  14. 2020Leveraging the Feature Distribution in Transfer-based Few-Shot Learningcited by 72
  15. 2018Multi-level Semantic Feature Augmentation for One-shot Learningcited by 62
  16. 2021Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learningcited by 49
  17. 2021Shallow Bayesian Meta Learning for Real-World Few-Shot Recognitioncited by 48
  18. 2020Enhancing Few-Shot Image Classification with Unlabelled Examplescited by 32
  19. 2020Self-Supervised Prototypical Transfer Learning for Few-Shot Classificationcited by 26
  20. 2022Self-Supervision Can Be a Good Few-Shot Learnercited by 19
  21. 2022Transductive Decoupled Variational Inference for Few-Shot Classificationcited by 7

Built on by

Cited by year

Citing papers per year: 2023: 3; 2024: 1.

4 papers.

  1. 2023On the Design Fundamentals of Diffusion Models: A Surveycited by 59
  2. 2023MetaDiff: Meta-Learning with Conditional Diffusion for Few-Shot Learningcited by 40
  3. 2023GeNIe: Generative Hard Negative Images Through Diffusioncited by 8
  4. 2024BECLR: Batch Enhanced Contrastive Few-Shot Learningcited by 6

Similar, not cited

Close in wording, no citation either way

  1. 2025Sparse-to-Sparse Training of Diffusion Models
  2. 2023A Comprehensive Survey on Knowledge Distillation of Diffusion Models
  3. 2022Few-Shot Diffusion Models
  4. 2024Few-shot Learner Parameterization by Diffusion Time-steps
  5. 2024An Overview of Diffusion Models: Applications, Guided Generation, Statistical Rates and Optimization
  6. 2023Patch Diffusion: Faster and More Data-Efficient Training of Diffusion Models
  7. 2024Towards Faster Training of Diffusion Models: An Inspiration of A Consistency Phenomenon
  8. 2026Multi-Objective Learning for Diffusion Models: A Statistical Theory under Semi-Supervised Learning