Active Generation for Image Classification

• URL: http://arxiv.org/abs/2403.06517v2
• Date: Thu, 15 Aug 2024 05:04:21 GMT
• Title: Active Generation for Image Classification
• Authors: Tao Huang, Jiaqi Liu, Shan You, Chang Xu,
• Abstract summary: 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.
• Score: 45.93535669217115
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, the growing capabilities of deep generative models have underscored their potential in enhancing image classification accuracy. However, existing methods often demand the generation of a disproportionately large number of images compared to the original dataset, while having only marginal improvements in accuracy. This computationally expensive and time-consuming process hampers the practicality of such approaches. In this paper, 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. It aims to create images akin to the challenging or misclassified samples encountered by the current model and incorporates these generated images into the training set to augment model performance. ActGen introduces an attentive image guidance technique, using real images as guides during the denoising process of a diffusion model. The model's attention on class prompt is leveraged to ensure the preservation of similar foreground object while diversifying the background. Furthermore, we introduce a gradient-based generation guidance method, which employs two losses to generate more challenging samples and prevent the generated images from being too similar to previously generated ones. Experimental results on the CIFAR and ImageNet datasets demonstrate that our method achieves better performance with a significantly reduced number of generated images. Code is available at https://github.com/hunto/ActGen.

Related papers

• Time Step Generating: A Universal Synthesized Deepfake Image Detector [0.4488895231267077]
  We propose a universal synthetic image detector Time Step Generating (TSG) TSG does not rely on pre-trained models' reconstructing ability, specific datasets, or sampling algorithms. We test the proposed TSG on the large-scale GenImage benchmark and it achieves significant improvements in both accuracy and generalizability.
  arXiv  Detail & Related papers  (2024-11-17T09:39:50Z)
• Reinforcing Pre-trained Models Using Counterfactual Images [54.26310919385808]
  This paper proposes a novel framework to reinforce classification models using language-guided generated counterfactual images. We identify model weaknesses by testing the model using the counterfactual image dataset. We employ the counterfactual images as an augmented dataset to fine-tune and reinforce the classification model.
  arXiv  Detail & Related papers  (2024-06-19T08:07:14Z)
• How to Trace Latent Generative Model Generated Images without Artificial Watermark? [88.04880564539836]
  Concerns have arisen regarding potential misuse related to images generated by latent generative models. We propose a latent inversion based method called LatentTracer to trace the generated images of the inspected model. Our experiments show that our method can distinguish the images generated by the inspected model and other images with a high accuracy and efficiency.
  arXiv  Detail & Related papers  (2024-05-22T05:33:47Z)
• Enhance Image Classification via Inter-Class Image Mixup with Diffusion Model [80.61157097223058]
  A prevalent strategy to bolster image classification performance is through augmenting the training set with synthetic images generated by T2I models. In this study, we scrutinize the shortcomings of both current generative and conventional data augmentation techniques. We introduce an innovative inter-class data augmentation method known as Diff-Mix, which enriches the dataset by performing image translations between classes.
  arXiv  Detail & Related papers  (2024-03-28T17:23:45Z)
• Class-Prototype Conditional Diffusion Model with Gradient Projection for Continual Learning [20.175586324567025]
  Mitigating catastrophic forgetting is a key hurdle in continual learning. A major issue is the deterioration in the quality of generated data compared to the original. We propose a GR-based approach for continual learning that enhances image quality in generators.
  arXiv  Detail & Related papers  (2023-12-10T17:39:42Z)
• Detecting Generated Images by Real Images Only [64.12501227493765]
  Existing generated image detection methods detect visual artifacts in generated images or learn discriminative features from both real and generated images by massive training. This paper approaches the generated image detection problem from a new perspective: Start from real images. By finding the commonality of real images and mapping them to a dense subspace in feature space, the goal is that generated images, regardless of their generative model, are then projected outside the subspace.
  arXiv  Detail & Related papers  (2023-11-02T03:09:37Z)
• Deep Image Fingerprint: Towards Low Budget Synthetic Image Detection and Model Lineage Analysis [8.777277201807351]
  We develop a new detection method for images that are indistinguishable from real ones. Our method can detect images from a known generative model and enable us to establish relationships between fine-tuned generative models. Our approach achieves comparable performance to state-of-the-art pre-trained detection methods on images generated by Stable Diffusion and Midversa.
  arXiv  Detail & Related papers  (2023-03-19T20:31:38Z)
• PAGER: Progressive Attribute-Guided Extendable Robust Image Generation [38.484332924924914]
  This work presents a generative modeling approach based on successive subspace learning (SSL) Unlike most generative models in the literature, our method does not utilize neural networks to analyze the underlying source distribution and synthesize images. The resulting method, called the progressive-guided extendable robust image generative (R) model, has advantages in mathematical transparency, progressive content generation, lower training time, robust performance with fewer training samples, and extendibility to conditional image generation.
  arXiv  Detail & Related papers  (2022-06-01T00:35:42Z)
• BIGRoC: Boosting Image Generation via a Robust Classifier [27.66648389933265]
  We propose a general model-agnostic technique for improving the image quality and the distribution fidelity of generated images. Our method, termed BIGRoC, is based on a post-processing procedure via the guidance of a given robust classifier.
  arXiv  Detail & Related papers  (2021-08-08T18:05:44Z)
• Counterfactual Generative Networks [59.080843365828756]
  We propose to decompose the image generation process into independent causal mechanisms that we train without direct supervision. By exploiting appropriate inductive biases, these mechanisms disentangle object shape, object texture, and background. We show that the counterfactual images can improve out-of-distribution with a marginal drop in performance on the original classification task.
  arXiv  Detail & Related papers  (2021-01-15T10:23:12Z)

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.