# Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labels

arXiv: https://arxiv.org/abs/2302.10586 · HTML: https://mlsanity.com/paper/2302.10586/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models.
- Builds on 95 of its 119 references that are in the map.
- First posted on arXiv 21 Feb 2023.

## Placed in

- Diffusion models / [Diffusion foundations and sampling](https://mlsanity.com/diffusion/diffusion-foundations-and-sampling/)

## Builds on (most cited first, 30 of 95)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Elucidating the Design Space of Diffusion-Based Generative Models](https://mlsanity.com/paper/2206.00364/) (2022)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)

## Built on by (most cited first, 30 of 37)

- [Dataset Diffusion: Diffusion-based Synthetic Dataset Generation for Pixel-Level Semantic Segmentation](https://mlsanity.com/paper/2309.14303/) (2023)
- [On the Design Fundamentals of Diffusion Models: A Survey](https://mlsanity.com/paper/2306.04542/) (2023)
- [Diffusion Models and Representation Learning: A Survey](https://mlsanity.com/paper/2407.00783/) (2024)
- [MissDiff: Training Diffusion Models on Tabular Data with Missing Values](https://mlsanity.com/paper/2307.00467/) (2023)
- [Fill-Up: Balancing Long-Tailed Data with Generative Models](https://mlsanity.com/paper/2306.07200/) (2023)
- [Feedback-guided Data Synthesis for Imbalanced Classification](https://mlsanity.com/paper/2310.00158/) (2023)
- [Identifying and Solving Conditional Image Leakage in Image-to-Video Diffusion Model](https://mlsanity.com/paper/2406.15735/) (2024)
- [Toward Understanding Generative Data Augmentation](https://mlsanity.com/paper/2305.17476/) (2023)
- [ScaleLong: Towards More Stable Training of Diffusion Model via Scaling Network Long Skip Connection](https://mlsanity.com/paper/2310.13545/) (2023)
- [Boosting Human-Object Interaction Detection with Text-to-Image Diffusion Model](https://mlsanity.com/paper/2305.12252/) (2023)
- [AI-Generated Images as Data Source: The Dawn of Synthetic Era](https://mlsanity.com/paper/2310.01830/) (2023)
- [MotionMix: Weakly-Supervised Diffusion for Controllable Motion Generation](https://mlsanity.com/paper/2401.11115/) (2024)
- [Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery](https://mlsanity.com/paper/2507.04051/) (2025)
- [Guided Diffusion from Self-Supervised Diffusion Features](https://mlsanity.com/paper/2312.08825/) (2023)
- [Cross-Domain Graph Data Scaling: A Showcase with Diffusion Models](https://mlsanity.com/paper/2406.01899/) (2024)
- [Dual Conditional Diffusion Models for Sequential Recommendation](https://mlsanity.com/paper/2410.21967/) (2024)
- [DiffAug: A Diffuse-and-Denoise Augmentation for Training Robust Classifiers](https://mlsanity.com/paper/2306.09192/) (2023)
- [Are Images Indistinguishable to Humans Also Indistinguishable to Classifiers?](https://mlsanity.com/paper/2405.18029/) (2024)
- [Diffusion-Refined VQA Annotations for Semi-Supervised Gaze Following](https://mlsanity.com/paper/2406.02774/) (2024)
- [MatWheel: Addressing Data Scarcity in Materials Science Through Synthetic Data](https://mlsanity.com/paper/2504.09152/) (2025)
- [DIVE: Inverting Conditional Diffusion Models for Discriminative Tasks](https://mlsanity.com/paper/2504.17253/) (2025)
- [Unsupervised Learning for Class Distribution Mismatch](https://mlsanity.com/paper/2505.06948/) (2025)
- [Robust Learning of Diffusion Models with Extremely Noisy Conditions](https://mlsanity.com/paper/2510.10149/) (2025)
- [Enhancing Semi-supervised Learning with Zero-shot Pseudolabels](https://mlsanity.com/paper/2502.12584/) (2025)
- [SARFormer -- An Acquisition Parameter Aware Vision Transformer for Synthetic Aperture Radar Data](https://mlsanity.com/paper/2504.08441/) (2025)
- [LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance](https://mlsanity.com/paper/2505.11703/) (2025)
- [Generative Data Augmentation for Object Point Cloud Segmentation](https://mlsanity.com/paper/2505.17783/) (2025)
- [Prompt-Driven Feature Diffusion for Open-World Semi-Supervised Learning](https://mlsanity.com/paper/2404.11795/) (2024)
- [LayerMatch: Do Pseudo-labels Benefit All Layers?](https://mlsanity.com/paper/2406.14207/) (2024)
- [Input-Adaptive Generative Dynamics in Diffusion Models](https://mlsanity.com/paper/2411.15199/) (2024)

## Abstract (arXiv)

In an effort to further advance semi-supervised generative and classification tasks, we propose a simple yet effective training strategy called dual pseudo training (DPT), built upon strong semi-supervised learners and diffusion models. DPT operates in three stages: training a classifier on partially labeled data to predict pseudo-labels; training a conditional generative model using these pseudo-labels to generate pseudo images; and retraining the classifier with a mix of real and pseudo images. Empirically, DPT consistently achieves SOTA performance of semi-supervised generation and classification across various settings. In particular, with one or two labels per class, DPT achieves a Fr'echet Inception Distance (FID) score of 3.08 or 2.52 on ImageNet 256x256. Besides, DPT outperforms competitive semi-supervised baselines substantially on ImageNet classification tasks, achieving top-1 accuracies of 59.0 (+2.8), 69.5 (+3.0), and 74.4 (+2.0) with one, two, or five labels per class, respectively. Notably, our results demonstrate that diffusion can generate realistic images with only a few labels (e.g., <0.1%) and generative augmentation remains viable for semi-supervised classification. Our code is available at https://github.com/ML-GSAI/DPT.