Word-Level Explanations for Analyzing Bias in Text-to-Image Models

• URL: http://arxiv.org/abs/2306.05500v1
• Date: Sat, 3 Jun 2023 21:39:07 GMT
• Title: Word-Level Explanations for Analyzing Bias in Text-to-Image Models
• Authors: Alexander Lin, Lucas Monteiro Paes, Sree Harsha Tanneru, Suraj Srinivas, Himabindu Lakkaraju
• Abstract summary: Text-to-image (T2I) models can generate images that underrepresent minorities based on race and sex. This paper investigates which word in the input prompt is responsible for bias in generated images.
• Score: 72.71184730702086
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text-to-image models take a sentence (i.e., prompt) and generate images associated with this input prompt. These models have created award wining-art, videos, and even synthetic datasets. However, text-to-image (T2I) models can generate images that underrepresent minorities based on race and sex. This paper investigates which word in the input prompt is responsible for bias in generated images. We introduce a method for computing scores for each word in the prompt; these scores represent its influence on biases in the model's output. Our method follows the principle of \emph{explaining by removing}, leveraging masked language models to calculate the influence scores. We perform experiments on Stable Diffusion to demonstrate that our method identifies the replication of societal stereotypes in generated images.

Related papers

• Image2Text2Image: A Novel Framework for Label-Free Evaluation of Image-to-Text Generation with Text-to-Image Diffusion Models [16.00576040281808]
  We propose a novel framework called Image2Text2Image to evaluate image captioning models. A high similarity score suggests that the model has produced a faithful textual description, while a low score highlights discrepancies. Our framework does not rely on human-annotated captions reference, making it a valuable tool for assessing image captioning models.
  arXiv  Detail & Related papers  (2024-11-08T17:07:01Z)
• Regeneration Based Training-free Attribution of Fake Images Generated by Text-to-Image Generative Models [39.33821502730661]
  We present a training-free method to attribute fake images generated by text-to-image models to their source models. By calculating and ranking the similarity of the test image and the candidate images, we can determine the source of the image.
  arXiv  Detail & Related papers  (2024-03-03T11:55:49Z)
• Contrastive Prompts Improve Disentanglement in Text-to-Image Diffusion Models [68.47333676663312]
  We show a simple modification of classifier-free guidance can help disentangle image factors in text-to-image models. The key idea of our method, Contrastive Guidance, is to characterize an intended factor with two prompts that differ in minimal tokens. We illustrate whose benefits in three scenarios: (1) to guide domain-specific diffusion models trained on an object class, (2) to gain continuous, rig-like controls for text-to-image generation, and (3) to improve the performance of zero-shot image editors.
  arXiv  Detail & Related papers  (2024-02-21T03:01:17Z)
• Fair Text-to-Image Diffusion via Fair Mapping [32.02815667307623]
  We propose a flexible, model-agnostic, and lightweight approach that modifies a pre-trained text-to-image diffusion model. By effectively addressing the issue of implicit language bias, our method produces more fair and diverse image outputs.
  arXiv  Detail & Related papers  (2023-11-29T15:02:01Z)
• ITI-GEN: Inclusive Text-to-Image Generation [56.72212367905351]
  This study investigates inclusive text-to-image generative models that generate images based on human-written prompts. We show that, for some attributes, images can represent concepts more expressively than text. We propose a novel approach, ITI-GEN, that leverages readily available reference images for Inclusive Text-to-Image GENeration.
  arXiv  Detail & Related papers  (2023-09-11T15:54:30Z)
• Discffusion: Discriminative Diffusion Models as Few-shot Vision and Language Learners [88.07317175639226]
  We propose a novel approach, Discriminative Stable Diffusion (DSD), which turns pre-trained text-to-image diffusion models into few-shot discriminative learners. Our approach mainly uses the cross-attention score of a Stable Diffusion model to capture the mutual influence between visual and textual information.
  arXiv  Detail & Related papers  (2023-05-18T05:41:36Z)
• Discriminative Class Tokens for Text-to-Image Diffusion Models [107.98436819341592]
  We propose a non-invasive fine-tuning technique that capitalizes on the expressive potential of free-form text. Our method is fast compared to prior fine-tuning methods and does not require a collection of in-class images. We evaluate our method extensively, showing that the generated images are: (i) more accurate and of higher quality than standard diffusion models, (ii) can be used to augment training data in a low-resource setting, and (iii) reveal information about the data used to train the guiding classifier.
  arXiv  Detail & Related papers  (2023-03-30T05:25:20Z)
• Zero-Shot Image-to-Text Generation for Visual-Semantic Arithmetic [72.60554897161948]
  Recent text-to-image matching models apply contrastive learning to large corpora of uncurated pairs of images and sentences. In this work, we repurpose such models to generate a descriptive text given an image at inference time. The resulting captions are much less restrictive than those obtained by supervised captioning methods.
  arXiv  Detail & Related papers  (2021-11-29T11:01:49Z)

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.