Training-Free Synthetic Data Generation with Dual IP-Adapter Guidance

• URL: http://arxiv.org/abs/2509.22635v1
• Date: Fri, 26 Sep 2025 17:57:32 GMT
• Title: Training-Free Synthetic Data Generation with Dual IP-Adapter Guidance
• Authors: Luc Boudier, Loris Manganelli, Eleftherios Tsonis, Nicolas Dufour, Vicky Kalogeiton,
• Abstract summary: DIPSY is a training-free approach to generate synthetic images using few-shot examples.<n>Our approach achieves state-of-the-art or comparable performance.<n>Our results highlight the effectiveness of leveraging dual image prompting with positive-negative guidance for generating class-discriminative features.
• Score: 13.893061390641348
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Few-shot image classification remains challenging due to the limited availability of labeled examples. Recent approaches have explored generating synthetic training data using text-to-image diffusion models, but often require extensive model fine-tuning or external information sources. We present a novel training-free approach, called DIPSY, that leverages IP-Adapter for image-to-image translation to generate highly discriminative synthetic images using only the available few-shot examples. DIPSY introduces three key innovations: (1) an extended classifier-free guidance scheme that enables independent control over positive and negative image conditioning; (2) a class similarity-based sampling strategy that identifies effective contrastive examples; and (3) a simple yet effective pipeline that requires no model fine-tuning or external captioning and filtering. Experiments across ten benchmark datasets demonstrate that our approach achieves state-of-the-art or comparable performance, while eliminating the need for generative model adaptation or reliance on external tools for caption generation and image filtering. Our results highlight the effectiveness of leveraging dual image prompting with positive-negative guidance for generating class-discriminative features, particularly for fine-grained classification tasks.

Related papers

• GMAIL: Generative Modality Alignment for generated Image Learning [51.071351994330605]
  We propose a novel framework for discriminative use of generated images, coined GMAIL, that explicitly treats generated images as a separate modality from real images.<n>Our framework can be easily incorporated with various vision-language models, and we demonstrate its efficacy throughout extensive experiments.
  arXiv  Detail & Related papers  (2026-02-17T05:40:25Z)
• Active Generation for Image Classification [45.93535669217115]
  We propose to address the efficiency of image generation by focusing on the specific needs and characteristics of the model. With a central tenet of active learning, our method, named ActGen, takes a training-aware approach to image generation.
  arXiv  Detail & Related papers  (2024-03-11T08:45:31Z)
• Diversified in-domain synthesis with efficient fine-tuning for few-shot classification [64.86872227580866]
  Few-shot image classification aims to learn an image classifier using only a small set of labeled examples per class. We propose DISEF, a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. We validate our method in ten different benchmarks, consistently outperforming baselines and establishing a new state-of-the-art for few-shot classification.
  arXiv  Detail & Related papers  (2023-12-05T17:18:09Z)
• DreamArtist++: Controllable One-Shot Text-to-Image Generation via Positive-Negative Adapter [63.622879199281705]
  Some example-based image generation approaches have been proposed, emphi.e. generating new concepts based on absorbing the salient features of a few input references.<n>We propose a simple yet effective framework, namely DreamArtist, which adopts a novel positive-negative prompt-tuning learning strategy on the pre-trained diffusion model.<n>We have conducted extensive experiments and evaluated the proposed method from image similarity (fidelity) and diversity, generation controllability, and style cloning.
  arXiv  Detail & Related papers  (2022-11-21T10:37:56Z)
• GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models [16.786221846896108]
  We explore diffusion models for the problem of text-conditional image synthesis and compare two different guidance strategies. We find that the latter is preferred by human evaluators for both photorealism and caption similarity, and often produces photorealistic samples. Our models can be fine-tuned to perform image inpainting, enabling powerful text-driven image editing.
  arXiv  Detail & Related papers  (2021-12-20T18:42:55Z)
• Meta Internal Learning [88.68276505511922]
  Internal learning for single-image generation is a framework, where a generator is trained to produce novel images based on a single image. We propose a meta-learning approach that enables training over a collection of images, in order to model the internal statistics of the sample image more effectively. Our results show that the models obtained are as suitable as single-image GANs for many common image applications.
  arXiv  Detail & Related papers  (2021-10-06T16:27:38Z)
• IMAGINE: Image Synthesis by Image-Guided Model Inversion [79.4691654458141]
  We introduce an inversion based method, denoted as IMAge-Guided model INvErsion (IMAGINE), to generate high-quality and diverse images. We leverage the knowledge of image semantics from a pre-trained classifier to achieve plausible generations. IMAGINE enables the synthesis procedure to simultaneously 1) enforce semantic specificity constraints during the synthesis, 2) produce realistic images without generator training, and 3) give users intuitive control over the generation process.
  arXiv  Detail & Related papers  (2021-04-13T02:00:24Z)
• Negative Data Augmentation [127.28042046152954]
  We show that negative data augmentation samples provide information on the support of the data distribution. We introduce a new GAN training objective where we use NDA as an additional source of synthetic data for the discriminator. Empirically, models trained with our method achieve improved conditional/unconditional image generation along with improved anomaly detection capabilities.
  arXiv  Detail & Related papers  (2021-02-09T20:28:35Z)
• Unlabeled Data Guided Semi-supervised Histopathology Image Segmentation [34.45302976822067]
  Semi-supervised learning (SSL) based on generative methods has been proven to be effective in utilizing diverse image characteristics. We propose a new data guided generative method for histopathology image segmentation by leveraging the unlabeled data distributions. Our method is evaluated on glands and nuclei datasets.
  arXiv  Detail & Related papers  (2020-12-17T02:54:19Z)

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.