Would Deep Generative Models Amplify Bias in Future Models?

• URL: http://arxiv.org/abs/2404.03242v1
• Date: Thu, 4 Apr 2024 06:58:39 GMT
• Title: Would Deep Generative Models Amplify Bias in Future Models?
• Authors: Tianwei Chen, Yusuke Hirota, Mayu Otani, Noa Garcia, Yuta Nakashima,
• Abstract summary: We investigate the impact of deep generative models on potential social biases in upcoming computer vision models. We conduct simulations by substituting original images in COCO and CC3M datasets with images generated through Stable Diffusion. Contrary to expectations, our findings indicate that introducing generated images during training does not uniformly amplify bias.
• Score: 29.918422914275226
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We investigate the impact of deep generative models on potential social biases in upcoming computer vision models. As the internet witnesses an increasing influx of AI-generated images, concerns arise regarding inherent biases that may accompany them, potentially leading to the dissemination of harmful content. This paper explores whether a detrimental feedback loop, resulting in bias amplification, would occur if generated images were used as the training data for future models. We conduct simulations by progressively substituting original images in COCO and CC3M datasets with images generated through Stable Diffusion. The modified datasets are used to train OpenCLIP and image captioning models, which we evaluate in terms of quality and bias. Contrary to expectations, our findings indicate that introducing generated images during training does not uniformly amplify bias. Instead, instances of bias mitigation across specific tasks are observed. We further explore the factors that may influence these phenomena, such as artifacts in image generation (e.g., blurry faces) or pre-existing biases in the original datasets.

Related papers

• Will the Inclusion of Generated Data Amplify Bias Across Generations in Future Image Classification Models? [29.71939692883025]
  We investigate the effects of generated data on image classification tasks, with a specific focus on bias. Hundreds of experiments are conducted on Colorized MNIST, CIFAR-20/100, and Hard ImageNet datasets. Our findings contribute to the ongoing debate on the implications of synthetic data for fairness in real-world applications.
  arXiv  Detail & Related papers  (2024-10-14T05:07:06Z)
• 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)
• Invisible Relevance Bias: Text-Image Retrieval Models Prefer AI-Generated Images [67.18010640829682]
  We show that AI-generated images introduce an invisible relevance bias to text-image retrieval models. The inclusion of AI-generated images in the training data of the retrieval models exacerbates the invisible relevance bias. We propose an effective training method aimed at alleviating the invisible relevance bias.
  arXiv  Detail & Related papers  (2023-11-23T16:22:58Z)
• Analyzing Bias in Diffusion-based Face Generation Models [75.80072686374564]
  Diffusion models are increasingly popular in synthetic data generation and image editing applications. We investigate the presence of bias in diffusion-based face generation models with respect to attributes such as gender, race, and age. We examine how dataset size affects the attribute composition and perceptual quality of both diffusion and Generative Adversarial Network (GAN) based face generation models.
  arXiv  Detail & Related papers  (2023-05-10T18:22:31Z)
• Fair Diffusion: Instructing Text-to-Image Generation Models on Fairness [15.059419033330126]
  We present a novel strategy, called Fair Diffusion, to attenuate biases after the deployment of generative text-to-image models. Specifically, we demonstrate shifting a bias, based on human instructions, in any direction yielding arbitrarily new proportions for, e.g., identity groups. This introduced control enables instructing generative image models on fairness, with no data filtering and additional training required.
  arXiv  Detail & Related papers  (2023-02-07T18:25:28Z)
• Debiasing Vision-Language Models via Biased Prompts [79.04467131711775]
  We propose a general approach for debiasing vision-language foundation models by projecting out biased directions in the text embedding. We show that debiasing only the text embedding with a calibrated projection matrix suffices to yield robust classifiers and fair generative models.
  arXiv  Detail & Related papers  (2023-01-31T20:09:33Z)
• Will Large-scale Generative Models Corrupt Future Datasets? [5.593352892211305]
  Large-scale text-to-image generative models can generate high-quality and realistic images from users' prompts. This paper empirically answers this question by simulating contamination. We conclude that generated images negatively affect downstream performance, while the significance depends on tasks and the amount of generated images.
  arXiv  Detail & Related papers  (2022-11-15T12:25:33Z)
• Unravelling the Effect of Image Distortions for Biased Prediction of Pre-trained Face Recognition Models [86.79402670904338]
  We evaluate the performance of four state-of-the-art deep face recognition models in the presence of image distortions. We have observed that image distortions have a relationship with the performance gap of the model across different subgroups.
  arXiv  Detail & Related papers  (2021-08-14T16:49:05Z)
• 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)
• Evaluating and Mitigating Bias in Image Classifiers: A Causal Perspective Using Counterfactuals [27.539001365348906]
  We present a method for generating counterfactuals by incorporating a structural causal model (SCM) in an improved variant of Adversarially Learned Inference (ALI) We show how to explain a pre-trained machine learning classifier, evaluate its bias, and mitigate the bias using a counterfactual regularizer.
  arXiv  Detail & Related papers  (2020-09-17T13:19:31Z)

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.