arXiv AI

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.

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

1d ago

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

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

arXiv AI

Jun 26

Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes

arXiv:2509. 09960v2 Announce Type: replace-cross Abstract: Synthetic tabular data generation is increasingly essential in machine learning, supporting downstream applications when real-world, high-quality tabular data is insufficient.

By Mingxuan Jiang, Keyang Chen, Yongxin Wang, Yongsheng Zhao, Ziyue Dai, Yicun Liu, Zeping Li, Qiuyang Zhang, Hongyi Nie, Hongbin Zhu, Sen Liu, Guangnan Ye, Hongfeng Chai

llms diffusion fine-tuning benchmarks safety

arXiv AI

Jun 10

ERAlign: Energy-based Representation Alignment of GNNs and LLMs on Text-attributed Graphs

arXiv:2606. 10461v1 Announce Type: cross Abstract: Text-attributed Graphs (TAGs) incorporate textual node attributes with graph structures to describe rich relational semantics.

By Xianlin Zeng, Fan Xia, Xiangyu Chen

llms rag diffusion benchmarks safety

arXiv AI

Jun 2

Improving Visual Representation Alignment Generation with GRPO

arXiv:2606. 00583v1 Announce Type: cross Abstract: Recent diffusion transformers have demonstrated strong image synthesis capabilities but remain inefficient to train due to weak alignment between generative and discriminative representations.

By Shentong Mo, Sukmin Yun

llms rag diffusion safety

arXiv AI

Jun 16

Efficient Flow Matching using Latent Variables

arXiv:2505. 04486v4 Announce Type: replace-cross Abstract: Flow matching models have shown great potential in image generation tasks among probabilistic generative models.

By Anirban Samaddar, Yixuan Sun, Viktor Nilsson, Sandeep Madireddy

diffusion multimodal benchmarks safety

arXiv Machine Learning

Jun 4

Geometry-Preserving Encoder/Decoder in Latent Generative Models

arXiv:2501. 09876v3 Announce Type: replace-cross Abstract: Generative modeling aims to generate new data samples that resemble a given dataset.

By Wonjun Lee, Riley C. W. O'Neill, Dongmian Zou, Jeff Calder, Gilad Lerman

diffusion benchmarks

arXiv AI

Jun 2

Better Source, Better Flow: Learning Condition-Dependent Source Distribution for Flow Matching

arXiv:2602. 05951v2 Announce Type: replace-cross Abstract: Flow matching has recently emerged as a promising alternative to diffusion-based generative models, particularly for text-to-image generation.

By Junwan Kim, Jiho Park, Seonghu Jeon, Seungryong Kim

diffusion benchmarks safety

arXiv Machine Learning

Aug 3

Cross-Resolution Semantic Learning for Graph Domain Adaptation

arXiv:2607. 29365v1 Announce Type: new Abstract: Graph Domain Adaptation (GDA) transfers predictive knowledge from labeled source graphs to unlabeled target graphs under distribution shift.

By Yingxu Wang, Haoze Huang, Zhongkai Zheng, Shangsong Liang

rag fine-tuning benchmarks

arXiv AI

Aug 5

Efficient unsupervised domain adaptation via self-supervised vision transformer and synergistic cross-domain alignment

arXiv:2407. 21311v2 Announce Type: replace-cross Abstract: Unsupervised domain adaptation (UDA) aims to mitigate domain shift, where the distribution of labeled source data differs from that of unlabeled target data.

By Ali Abedi, Q. M. Jonathan Wu, Ning Zhang, Farhad Pourpanah

llms computer-vision fine-tuning safety

arXiv Machine Learning

Jun 5

Zero-Flow Encoders

arXiv:2602. 00797v2 Announce Type: replace-cross Abstract: Flow-based methods have achieved significant success in various generative modeling tasks, capturing nuanced details within complex data distributions.

By Yakun Wang, Leyang Wang, Song Liu, Taiji Suzuki

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