LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance


Jae Myung Kim (University of Tuebingen), Stephan Alaniz (Télécom Paris), Cordelia Schmid (Inria, Ecole normale supérieure), Zeynep Akata (Technische Universität München)
The 35th British Machine Vision Conference
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Abstract

Despite recent advances in text-to-image generation, using synthetically generated data seldom brings a significant boost in performance for supervised learning. Oftentimes, synthetic datasets do not faithfully recreate the data distribution of real data, i.e., they lack the fidelity or diversity needed for effective downstream model training. While previous work has employed few-shot guidance to address this issue, existing methods still fail to capture and generate features unique to specific real images. In this paper, we introduce a novel dataset generation framework named LoFT, LoRA-Fused Training-data Generation with Few-shot Guidance. Our method fine-tunes LoRA weights on individual real images and fuses them at inference time, producing synthetic images that combine the features of real images for improved diversity and fidelity of generated data. We evaluate the synthetic data produced by LoFT on 10 datasets, using 8 to 64 real images per class as guidance and scaling up to 1000 images per class. Our experiments show that training on LoFT-generated data consistently outperforms other synthetic dataset methods, significantly increasing accuracy as the dataset size increases. Additionally, our analysis demonstrates that LoFT generates datasets with high fidelity and sufficient diversity, which contribute to the performance improvement. The code is available at https://github.com/ExplainableML/LoFT.

Citation

@inproceedings{Kim_2025_BMVC,
author    = {Jae Myung Kim and Stephan Alaniz and Cordelia Schmid and Zeynep Akata},
title     = {LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance},
booktitle = {36th British Machine Vision Conference 2025, {BMVC} 2025, Sheffield, UK, November 24-27, 2025},
publisher = {BMVA},
year      = {2025},
url       = {https://bmva-archive.org.uk/bmvc/2025/assets/papers/Paper_852/paper.pdf}
}


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