arXiv AI July 21, 2026 By Jiaqi Zhu, Xincheng Chen, Yuncheng Wu, Zhaojing Luo, Beng Chin Ooi

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift

arXiv:2607. 18072v1 Announce Type: cross Abstract: Generative models trained on a source domain often produce samples that are poorly aligned with shifted target domains, limiting their effectiveness for target-domain data augmentation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

diffusion

arXiv Machine Learning

Jul 7

WeightCLIP: Aligning Datasets and Models for Weight Space Learning

arXiv:2607. 03551v1 Announce Type: new Abstract: Weight space learning aims to learn representations of neural network (NN) weights, enabling different downstream tasks.

By Aron Asefaw, Konstantinos Tzevelekakis, Damian Falk, L\'eo Meynent, Damian Borth

diffusion fine-tuning

arXiv AI

Aug 18

Bringing Generative Learning to Representation Learning: Self-Supervised Transfer Learning as Distribution Matching

arXiv:2502. 14424v3 Announce Type: replace-cross Abstract: Most self-supervised learning objectives defend against collapse but leave the target representation law unspecified.

By Yuling Jiao, Wensen Ma, Defeng Sun, Hansheng Wang, Yang Wang

fine-tuning benchmarks

arXiv Machine Learning

1d ago

A Flow Matching Algorithm for Many-Shot Adaptation to Unseen Distributions

arXiv:2605.06272v2 Announce Type: replace Abstract: While generative modeling has achieved remarkable success on tasks like natural language-conditioned image generation, enabling model adaptation fr...

By Tyler Ingebrand, Ruihan Zhao, Kushagra Gupta, David Fridovich-Keil, Sandeep P. Chinchali, Ufuk Topcu

diffusion

arXiv AI

1d ago

Rethinking Data Augmentation under Covariate Shift: Invariant-Guided Diffusion and Prototype Reweighting

arXiv:2610.00873v1 Announce Type: cross Abstract: In many industrial applications, 1) tabular data is scarce and imbalanced and thus requires synthetic expansion; 2) input distributions drift between...

By Hongyu Cao, Xinyuan Wang, Arun Vignesh Malarkkan, Kunpeng Liu, Haifeng Chen, Yanjie Fu

diffusion data-engineering

arXiv Computer Vision

Sep 7

PAPT++: Risk-Aware Adversarial Tuning and Generation for Single Domain Generalization

PAPT++ is a risk‑aware adversarial generation‑training framework designed to improve single domain generalization. It learns diverse semantic reference images per class and uses them as denoising targets in classifier‑guided diffusion synthesis, thereby generating challenging yet semantically consistent samples. These samples are iteratively combined with source data to update the classifier, progressively exposing it to difficult variations and enhancing generalization performance on standard benchmarks.

By Zhipeng Xu, De Cheng, Xinyang Jiang, Lingfeng He, Huaijie Wang, Dongsheng Li, Nannan Wang, Xinbo Gao

diffusion multimodal benchmarks safety

arXiv Machine Learning

Jun 2

Efficient Synthetic Network Generation via Latent Embedding Reconstruction

arXiv:2606. 00934v1 Announce Type: cross Abstract: Network data are ubiquitous across the social sciences, biology, and information systems.

By Feifan Jiang, Yinan Bu, Shihao Wu, Gongjun Xu, Ji Zhu

rag diffusion efficiency

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WeightCLIP: Aligning Datasets and Models for Weight Space Learning

Bringing Generative Learning to Representation Learning: Self-Supervised Transfer Learning as Distribution Matching

A Flow Matching Algorithm for Many-Shot Adaptation to Unseen Distributions

Rethinking Data Augmentation under Covariate Shift: Invariant-Guided Diffusion and Prototype Reweighting

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