Investigating the Impact of Biased Image Generation on Future Computer Vision Models

Core Concepts

The use of generated images from deep generative models like Stable Diffusion during training does not consistently amplify bias in downstream computer vision models. Factors like inherent biases in the original datasets and limitations of current image generation models can influence the observed bias trends.

Abstract

The paper investigates the impact of using images generated by deep generative models like Stable Diffusion as training data for future computer vision models. The authors conduct experiments by progressively replacing original images in the COCO and CC3M datasets with generated images and evaluating the bias in two downstream tasks: image-text pretraining with OpenCLIP and image captioning. The key findings are: Bias trends are inconsistent - the authors observe instances of both bias amplification and bias mitigation across different demographic attributes (gender, ethnicity, age, skin tone) and tasks. Potential reasons for the observed bias trends: Inherent biases in the original datasets, which may align with the biases in the generated images, leading to no amplification. Limitations of Stable Diffusion, such as blurry faces and stereotypical associations, which can influence the model's learning of demographic attributes. The authors provide recommendations for handling biased generated images, including bias-filtering preprocessing and caution regarding generation issues. The paper highlights the complex dynamics between image generation models and existing datasets, and the need for careful consideration of bias when incorporating synthetic data into the training of future computer vision models.

Stats

"Training images are frequently scraped from the internet with minimal efforts to filter out problematic samples and address representational disparities." "Biases in the dataset-creation process are introduced from three main sources: (1) biases inherited from the original population of images on the internet, (2) additional biases introduced by the image descriptions, and (3) biases introduced by the filtering process."

Quotes

"Coupled with the increasing concerns about the presence of social bias in deep generative models, this raises the following question: What consequences might arise if images generated by biased models become increasingly involved in the training process of future models?" "Overall, the key contributions of this paper are: 1) We show that, under our experimental setup, generated images from current deep generative models do not consistently amplify bias. 2) Through a set of follow-up experiments, we explore the underlying reasons behind these results, offering valuable insights into the dynamics between image generation models and existing datasets."

Key Insights Distilled From

Would Deep Generative Models Amplify Bias in Future Models?

by Tianwei Chen... at arxiv.org 04-05-2024

https://arxiv.org/pdf/2404.03242.pdf

Deeper Inquiries

How might the findings change if the experiments were conducted on larger-scale datasets like LAION-400M or LAION-5B?

Conducting the experiments on larger-scale datasets like LAION-400M or LAION-5B could potentially lead to different findings due to the increased complexity and diversity of the data. With a larger dataset, the impact of generated images on bias amplification or mitigation may vary. The scale of the dataset could influence the generalization of the results and provide a more comprehensive understanding of how synthetic images affect bias in model training. Additionally, larger datasets may reveal subtler biases or patterns that were not as evident in smaller datasets like COCO and CC3M. The performance of models trained on such extensive datasets may also differ, affecting the overall assessment of bias amplification or mitigation.

What other types of bias, beyond the demographic attributes considered in this study, could be impacted by the use of generated images in model training?

In addition to demographic attributes like gender, age, ethnicity, and skin tone, the use of generated images in model training could impact various other types of bias. Some examples include: Socioeconomic Bias: Generated images may inadvertently reinforce stereotypes related to socioeconomic status, such as depicting certain occupations or living conditions in a biased manner. Ability Bias: Images generated by models could perpetuate biases related to physical or cognitive abilities, potentially reinforcing stereotypes or misconceptions about individuals with disabilities. Cultural Bias: Generated images may reflect cultural stereotypes or biases, influencing how certain cultures or traditions are portrayed in visual content. Appearance Bias: Biases related to physical appearance, body size, or beauty standards could be reinforced through generated images, impacting how individuals are represented in visual media. Geographical Bias: Images generated by models may inadvertently exhibit biases towards specific regions or countries, leading to skewed representations of different geographical locations.

How can the limitations of current image generation models, such as blurry faces and stereotypical associations, be addressed to mitigate their influence on downstream bias?

To address the limitations of current image generation models and mitigate their influence on downstream bias, several strategies can be implemented: Improved Training Data: Providing diverse and representative training data to the image generation models can help reduce biases and improve the quality of generated images. Bias Detection Algorithms: Implementing bias detection algorithms during the image generation process can help identify and mitigate potential biases before the images are used for downstream tasks. Regular Model Evaluation: Continuously evaluating the performance of image generation models and monitoring for biases can help in identifying and addressing issues promptly. Diverse Prompting: Using diverse and inclusive prompts during image generation can help steer the models away from stereotypical associations and encourage the generation of more varied and unbiased images. Ethical Guidelines: Establishing ethical guidelines and standards for image generation models can help ensure responsible and unbiased use of generated images in downstream applications. Regular audits and reviews can also help in maintaining ethical standards.