Amortized Moment Matching for Visual Generation

• URL: http://arxiv.org/abs/2607.26860v1
• Date: Wed, 29 Jul 2026 12:44:19 GMT
• Title: Amortized Moment Matching for Visual Generation
• Abstract summary: We propose amortized moment matching, utilizing neural networks to learn data moments as distributional training signals.<n>We instantiate the Amortized Fréchet Distance (AMFD) loss.
• Score: 27.680148307313186
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose amortized moment matching, utilizing neural networks to learn data moments as distributional training signals. By casting diffusion denoisers through polynomial projections, we establish a general framework for moment amortization, revealing that an $n$-th degree projection explicitly identifies data moments up to order $n+1$. Derived from the tractable affine case, we instantiate the Amortized Fréchet Distance (AMFD) loss. Unlike FD-loss which relies on explicit marginal moment calculations, AMFD is able to dynamically learn conditional moments via an alternating, matrix-free optimization pipeline that effortlessly scales to high-dimensional data. When operating on global representation features, AMFD serves as a powerful post-training objective; empirically, its neural formulation yields more robust training dynamics than exact statistical matching, substantially surpassing the FD baseline on the FDr$^6$ metric and achieving superior one-step generation on ImageNet. Furthermore, it unlocks direct exploration within native generative spaces, suggesting that the first two moments can identify target distributions only in spaces with strong semantics. Finally, when scaled to text-to-image generation, the condition-aware nature of AMFD unlocks massive gains in instruction-following capabilities, enabling our one-step models to outperform their multi-step FLUX.2 [klein] 4B teachers on the GenEval benchmark while achieving on-par performance on PickScore. Code and checkpoints are available at https://github.com/poppuppy/amfd.

Related papers

• From Unfamiliar to Familiar: Detecting Pre-training Data via Gradient Deviations in Large Language Models [24.38939297043815]
  Existing methods mainly focus on the likelihood-based statistical features or signals before and after fine-tuning.<n>We propose GDS, a method that identifies pre-training data by probing Gradient Deviation Scores of target samples.<n>GDS achieves state-of-the-art performance with significantly improved cross-dataset transferability over strong baselines.
  arXiv  Detail & Related papers  (2026-03-05T05:21:51Z)
• Fast and Scalable Analytical Diffusion [43.4234198410875]
  Analytical diffusion models offer a mathematically transparent path to generative modeling by formulating the denoising score as an empirical-Bayes posterior mean.<n>Standard formulation requires a full-dataset scan at every timestep, scaling linearly with dataset size.<n>We propose Dynamic Time-Aware Golden Subset Diffusion (GoldDiff), a training-free framework that decouples inference from dataset size.
  arXiv  Detail & Related papers  (2026-02-18T14:41:09Z)
• Self-Supervised Learning via Flow-Guided Neural Operator on Time-Series Data [57.85958428020496]
  Flow-Guided Neural Operator (FGNO) is a novel framework combining operator learning with flow matching for SSL training.<n>FGNO learns mappings in functional spaces by using Short-Time Fourier Transform to unify different time resolutions.<n>Unlike prior generative SSL methods that use noisy inputs during inference, we propose using clean inputs for representation extraction while learning representations with noise.
  arXiv  Detail & Related papers  (2026-02-12T18:54:57Z)
• FAIL: Flow Matching Adversarial Imitation Learning for Image Generation [52.643484089126844]
  Post-training of flow matching models-aligning the output distribution with a high-quality target-is mathematically equivalent to Imitation learning.<n>We propose Flow Matching Adrial Learning (FAIL), which minimizes policy-expert divergence through adversarial training without explicit rewards or pairwise comparisons.
  arXiv  Detail & Related papers  (2026-02-12T16:36:33Z)
• ESPADA: Execution Speedup via Semantics Aware Demonstration Data Downsampling for Imitation Learning [18.435889278351297]
  ESPADA is a semantically aware framework that segments demonstrations using a VLM-LLM pipeline with 3D gripper-object relations.<n>To scale from a single annotated episode to the full dataset, ESPADA propagates segment labels via Dynamic Time Warping.<n> ESPADA achieves approximately a 2x speed-up while maintaining success rates, narrowing the gap between human demonstrations and efficient robot control.
  arXiv  Detail & Related papers  (2025-12-08T10:08:33Z)
• Private Training & Data Generation by Clustering Embeddings [74.00687214400021]
  Differential privacy (DP) provides a robust framework for protecting individual data.<n>We introduce a novel principled method for DP synthetic image embedding generation.<n> Empirically, a simple two-layer neural network trained on synthetically generated embeddings achieves state-of-the-art (SOTA) classification accuracy.
  arXiv  Detail & Related papers  (2025-06-20T00:17:14Z)
• SuperFlow++: Enhanced Spatiotemporal Consistency for Cross-Modal Data Pretraining [62.433137130087445]
  SuperFlow++ is a novel framework that integrates pretraining and downstream tasks using consecutive camera pairs.<n>We show that SuperFlow++ outperforms state-of-the-art methods across diverse tasks and driving conditions.<n>With strong generalizability and computational efficiency, SuperFlow++ establishes a new benchmark for data-efficient LiDAR-based perception in autonomous driving.
  arXiv  Detail & Related papers  (2025-03-25T17:59:57Z)
• USDRL: Unified Skeleton-Based Dense Representation Learning with Multi-Grained Feature Decorrelation [24.90512145836643]
  We introduce a Unified Skeleton-based Dense Representation Learning framework based on feature decorrelation.<n>We show that our approach significantly outperforms the current state-of-the-art (SOTA) approaches.
  arXiv  Detail & Related papers  (2024-12-12T12:20:27Z)
• Assessing Neural Network Representations During Training Using Noise-Resilient Diffusion Spectral Entropy [55.014926694758195]
  Entropy and mutual information in neural networks provide rich information on the learning process. We leverage data geometry to access the underlying manifold and reliably compute these information-theoretic measures. We show that they form noise-resistant measures of intrinsic dimensionality and relationship strength in high-dimensional simulated data.
  arXiv  Detail & Related papers  (2023-12-04T01:32:42Z)
• Value function estimation using conditional diffusion models for control [62.27184818047923]
  We propose a simple algorithm called Diffused Value Function (DVF) It learns a joint multi-step model of the environment-robot interaction dynamics using a diffusion model. We show how DVF can be used to efficiently capture the state visitation measure for multiple controllers.
  arXiv  Detail & Related papers  (2023-06-09T18:40:55Z)
• Large Scale Time-Series Representation Learning via Simultaneous Low and High Frequency Feature Bootstrapping [7.0064929761691745]
  We propose a non-contrastive self-supervised learning approach efficiently captures low and high-frequency time-varying features. Our method takes raw time series data as input and creates two different augmented views for two branches of the model. To demonstrate the robustness of our model we performed extensive experiments and ablation studies on five real-world time-series datasets.
  arXiv  Detail & Related papers  (2022-04-24T14:39:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.