GradBias: Unveiling Word Influence on Bias in Text-to-Image Generative Models

• URL: http://arxiv.org/abs/2408.16700v1
• Date: Thu, 29 Aug 2024 16:51:07 GMT
• Title: GradBias: Unveiling Word Influence on Bias in Text-to-Image Generative Models
• Authors: Moreno D'Incà, Elia Peruzzo, Massimiliano Mancini, Xingqian Xu, Humphrey Shi, Nicu Sebe,
• Abstract summary: We propose a framework to identify, quantify, and explain biases in an open set setting. This pipeline leverages a Large Language Model (LLM) to propose biases starting from a set of captions. We show two variations of this framework: OpenBias and GradBias.
• Score: 75.04426753720553
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recent progress in Text-to-Image (T2I) generative models has enabled high-quality image generation. As performance and accessibility increase, these models are gaining significant attraction and popularity: ensuring their fairness and safety is a priority to prevent the dissemination and perpetuation of biases. However, existing studies in bias detection focus on closed sets of predefined biases (e.g., gender, ethnicity). In this paper, we propose a general framework to identify, quantify, and explain biases in an open set setting, i.e. without requiring a predefined set. This pipeline leverages a Large Language Model (LLM) to propose biases starting from a set of captions. Next, these captions are used by the target generative model for generating a set of images. Finally, Vision Question Answering (VQA) is leveraged for bias evaluation. We show two variations of this framework: OpenBias and GradBias. OpenBias detects and quantifies biases, while GradBias determines the contribution of individual prompt words on biases. OpenBias effectively detects both well-known and novel biases related to people, objects, and animals and highly aligns with existing closed-set bias detection methods and human judgment. GradBias shows that neutral words can significantly influence biases and it outperforms several baselines, including state-of-the-art foundation models. Code available here: https://github.com/Moreno98/GradBias.

Related papers

• Debiasing Classifiers by Amplifying Bias with Latent Diffusion and Large Language Models [9.801159950963306]
  We introduce DiffuBias, a novel pipeline for text-to-image generation that enhances classifier robustness by generating bias-conflict samples. DrouBias is the first approach leveraging a stable diffusion model to generate bias-conflict samples in debiasing tasks. Our comprehensive experimental evaluations demonstrate that DiffuBias achieves state-of-the-art performance on benchmark datasets.
  arXiv  Detail & Related papers  (2024-11-25T04:11:16Z)
• Gender Bias Evaluation in Text-to-image Generation: A Survey [25.702257177921048]
  We review recent work on gender bias evaluation in text-to-image generation. We focus on the evaluation of recent popular models such as Stable Diffusion and DALL-E 2.
  arXiv  Detail & Related papers  (2024-08-21T06:01:23Z)
• BIGbench: A Unified Benchmark for Social Bias in Text-to-Image Generative Models Based on Multi-modal LLM [8.24274551090375]
  We introduce BIGbench, a unified benchmark for Biases of Image Generation. Unlike existing benchmarks, BIGbench classifies and evaluates biases across four dimensions. Our study also reveal new research directions about biases, such as the effect of distillation and irrelevant protected attributes.
  arXiv  Detail & Related papers  (2024-07-21T18:09:40Z)
• OpenBias: Open-set Bias Detection in Text-to-Image Generative Models [108.2219657433884]
  We tackle the challenge of open-set bias detection in text-to-image generative models presenting OpenBias. OpenBias identifies and quantifies the severity of biases agnostically, without access to any precompiled set. We study the behavior of Stable Diffusion 1.5, 2, and XL emphasizing new biases, never investigated before.
  arXiv  Detail & Related papers  (2024-04-11T17:59:56Z)
• Quantifying Bias in Text-to-Image Generative Models [49.60774626839712]
  Bias in text-to-image (T2I) models can propagate unfair social representations and may be used to aggressively market ideas or push controversial agendas. Existing T2I model bias evaluation methods only focus on social biases. We propose an evaluation methodology to quantify general biases in T2I generative models, without any preconceived notions.
  arXiv  Detail & Related papers  (2023-12-20T14:26:54Z)
• Balancing the Picture: Debiasing Vision-Language Datasets with Synthetic Contrast Sets [52.77024349608834]
  Vision-language models can perpetuate and amplify societal biases learned during pre-training on uncurated image-text pairs from the internet. COCO Captions is the most commonly used dataset for evaluating bias between background context and the gender of people in-situ. We propose a novel dataset debiasing pipeline to augment the COCO dataset with synthetic, gender-balanced contrast sets.
  arXiv  Detail & Related papers  (2023-05-24T17:59:18Z)
• 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)
• Discovering and Mitigating Visual Biases through Keyword Explanation [66.71792624377069]
  We propose the Bias-to-Text (B2T) framework, which interprets visual biases as keywords. B2T can identify known biases, such as gender bias in CelebA, background bias in Waterbirds, and distribution shifts in ImageNet-R/C. B2T uncovers novel biases in larger datasets, such as Dollar Street and ImageNet.
  arXiv  Detail & Related papers  (2023-01-26T13:58:46Z)

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.