# Denoising with a Joint-Embedding Predictive Architecture

arXiv: https://arxiv.org/abs/2410.03755 · HTML: https://mlsanity.com/paper/2410.03755/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models.
- Builds on 109 of its 125 references that are in the map.
- First posted on arXiv 2 Oct 2024.

## Placed in

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

## Builds on (most cited first, 30 of 109)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [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)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [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)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [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)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [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)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Elucidating the Design Space of Diffusion-Based Generative Models](https://mlsanity.com/paper/2206.00364/) (2022)

## Built on by (most cited first, 14 of 14)

- [[MASK] is All You Need](https://mlsanity.com/paper/2412.06787/) (2024)
- [LayerSync: Self-aligning Intermediate Layers](https://mlsanity.com/paper/2510.12581/) (2025)
- [JEPA-Reasoner: Decoupling Latent Reasoning from Token Generation](https://mlsanity.com/paper/2512.19171/) (2025)
- [High-Resolution Image Synthesis via Next-Token Prediction](https://mlsanity.com/paper/2411.14808/) (2024)
- [Discrete JEPA: Learning Discrete Token Representations without Reconstruction](https://mlsanity.com/paper/2506.14373/) (2025)
- [From Alignment to Prediction: A Study of Self-Supervised Learning and Predictive Representation Learning](https://mlsanity.com/paper/2604.13518/) (2026)
- [Beyond Representation Learning: A Systematic Study of Joint-Embedding Predictive Generation for 3D Brain MRI](https://mlsanity.com/paper/2608.28787/) (2026)
- [JEPA-T: Joint-Embedding Predictive Architecture with Text Fusion for Image Generation](https://mlsanity.com/paper/2510.00974/) (2025)
- [AGE: Adaptive-masking for Graph Embedding in Graph Retrieval-Augmented Generation](https://mlsanity.com/paper/2607.00052/) (2026)
- [Optimizing ML Workload Partitioning between CPUs and CIM Accelerators for Heterogeneous Computing](https://mlsanity.com/paper/2607.05240/) (2026)
- [RepFlow: Reciprocal Supervision Improves Generation and Representation in Flow Models](https://mlsanity.com/paper/2609.33217/) (2026)
- [One Latent, Many Tokens: Jointly Learning Compressed Embeddings for Efficient Language Diffusion](https://mlsanity.com/paper/2609.33698/) (2026)
- [EpicWorldModel: Exploration-driven Planning with Latent World Models](https://mlsanity.com/paper/2610.05996/) (2026)
- [Enhancing Latent World Models with Associative Memory](https://mlsanity.com/paper/2610.19683/) (2026)

## Abstract (arXiv)

Joint-embedding predictive architectures (JEPAs) have shown substantial promise in self-supervised representation learning, yet their application in generative modeling remains underexplored. Conversely, diffusion models have demonstrated significant efficacy in modeling arbitrary probability distributions. In this paper, we introduce Denoising with a Joint-Embedding Predictive Architecture (D-JEPA), pioneering the integration of JEPA within generative modeling. By recognizing JEPA as a form of masked image modeling, we reinterpret it as a generalized next-token prediction strategy, facilitating data generation in an auto-regressive manner. Furthermore, we incorporate diffusion loss to model the per-token probability distribution, enabling data generation in a continuous space. We also adapt flow matching loss as an alternative to diffusion loss, thereby enhancing the flexibility of D-JEPA. Empirically, with increased GFLOPs, D-JEPA consistently achieves lower FID scores with fewer training epochs, indicating its good scalability. Our base, large, and huge models outperform all previous generative models across all scales on ImageNet conditional generation benchmarks. Beyond image generation, D-JEPA is well-suited for other continuous data modeling, including video and audio.