DiffuGen: Adaptable Approach for Generating Labeled Image Datasets using Stable Diffusion Models

• URL: http://arxiv.org/abs/2309.00248v1
• Date: Fri, 1 Sep 2023 04:42:03 GMT
• Title: DiffuGen: Adaptable Approach for Generating Labeled Image Datasets using Stable Diffusion Models
• Authors: Michael Shenoda, Edward Kim
• Abstract summary: "DiffuGen" is a simple and adaptable approach that harnesses the power of stable diffusion models to create labeled image datasets efficiently. By leveraging stable diffusion models, our approach not only ensures the quality of generated datasets but also provides a versatile solution for label generation.
• Score: 2.0935496890864207
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Generating high-quality labeled image datasets is crucial for training accurate and robust machine learning models in the field of computer vision. However, the process of manually labeling real images is often time-consuming and costly. To address these challenges associated with dataset generation, we introduce "DiffuGen," a simple and adaptable approach that harnesses the power of stable diffusion models to create labeled image datasets efficiently. By leveraging stable diffusion models, our approach not only ensures the quality of generated datasets but also provides a versatile solution for label generation. In this paper, we present the methodology behind DiffuGen, which combines the capabilities of diffusion models with two distinct labeling techniques: unsupervised and supervised. Distinctively, DiffuGen employs prompt templating for adaptable image generation and textual inversion to enhance diffusion model capabilities.

Related papers

• A Simple Background Augmentation Method for Object Detection with Diffusion Model [53.32935683257045]
  In computer vision, it is well-known that a lack of data diversity will impair model performance. We propose a simple yet effective data augmentation approach by leveraging advancements in generative models. Background augmentation, in particular, significantly improves the models' robustness and generalization capabilities.
  arXiv  Detail & Related papers  (2024-08-01T07:40:00Z)
• Regularized Training with Generated Datasets for Name-Only Transfer of Vision-Language Models [36.59260354292177]
  Recent advancements in text-to-image generation have inspired researchers to generate datasets tailored for perception models using generative models. We aim to fine-tune vision-language models to a specific classification model without access to any real images. Despite the high fidelity of generated images, we observed a significant performance degradation when fine-tuning the model using the generated datasets.
  arXiv  Detail & Related papers  (2024-06-08T10:43:49Z)
• Distribution-Aware Data Expansion with Diffusion Models [55.979857976023695]
  We propose DistDiff, a training-free data expansion framework based on the distribution-aware diffusion model. DistDiff consistently enhances accuracy across a diverse range of datasets compared to models trained solely on original data.
  arXiv  Detail & Related papers  (2024-03-11T14:07:53Z)
• Steered Diffusion: A Generalized Framework for Plug-and-Play Conditional Image Synthesis [62.07413805483241]
  Steered Diffusion is a framework for zero-shot conditional image generation using a diffusion model trained for unconditional generation. We present experiments using steered diffusion on several tasks including inpainting, colorization, text-guided semantic editing, and image super-resolution.
  arXiv  Detail & Related papers  (2023-09-30T02:03:22Z)
• DiffDis: Empowering Generative Diffusion Model with Cross-Modal Discrimination Capability [75.9781362556431]
  We propose DiffDis to unify the cross-modal generative and discriminative pretraining into one single framework under the diffusion process. We show that DiffDis outperforms single-task models on both the image generation and the image-text discriminative tasks.
  arXiv  Detail & Related papers  (2023-08-18T05:03:48Z)
• DomainStudio: Fine-Tuning Diffusion Models for Domain-Driven Image Generation using Limited Data [20.998032566820907]
  This paper proposes a novel DomainStudio approach to adapt DDPMs pre-trained on large-scale source datasets to target domains using limited data. It is designed to keep the diversity of subjects provided by source domains and get high-quality and diverse adapted samples in target domains.
  arXiv  Detail & Related papers  (2023-06-25T07:40:39Z)
• Phoenix: A Federated Generative Diffusion Model [6.09170287691728]
  Training generative models on large centralized datasets can pose challenges in terms of data privacy, security, and accessibility. This paper proposes a novel method for training a Denoising Diffusion Probabilistic Model (DDPM) across multiple data sources using Federated Learning (FL) techniques.
  arXiv  Detail & Related papers  (2023-06-07T01:43:09Z)
• Conditional Generation from Unconditional Diffusion Models using Denoiser Representations [94.04631421741986]
  We propose adapting pre-trained unconditional diffusion models to new conditions using the learned internal representations of the denoiser network. We show that augmenting the Tiny ImageNet training set with synthetic images generated by our approach improves the classification accuracy of ResNet baselines by up to 8%.
  arXiv  Detail & Related papers  (2023-06-02T20:09:57Z)
• Training on Thin Air: Improve Image Classification with Generated Data [28.96941414724037]
  Diffusion Inversion is a simple yet effective method to generate diverse, high-quality training data for image classification. Our approach captures the original data distribution and ensures data coverage by inverting images to the latent space of Stable Diffusion. We identify three key components that allow our generated images to successfully supplant the original dataset.
  arXiv  Detail & Related papers  (2023-05-24T16:33:02Z)
• LayoutDiffuse: Adapting Foundational Diffusion Models for Layout-to-Image Generation [24.694298869398033]
  Our method trains efficiently, generates images with both high perceptual quality and layout alignment. Our method significantly outperforms other 10 generative models based on GANs, VQ-VAE, and diffusion models.
  arXiv  Detail & Related papers  (2023-02-16T14:20:25Z)
• Self-Guided Diffusion Models [53.825634944114285]
  We propose a framework for self-guided diffusion models. Our method provides guidance signals at various image granularities. Our experiments on single-label and multi-label image datasets demonstrate that self-labeled guidance always outperforms diffusion models without guidance.
  arXiv  Detail & Related papers  (2022-10-12T17:57:58Z)

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.