Multimodal Image Editing With Identity-Preserving Bias Mitigation
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Solution Overview
Problem
Conventional image generation models generate images with biased depictions of individuals due to inherent correlations in text embeddings, leading to unexpected gender, ethnicity, and other social biases, which can have negative impacts.
Innovation Solution
A machine learning model that includes an attribute classifier to identify biased depictions and calculates an identity-preserving loss to optimize latent codes, generating synthetic images that accurately preserve the identity of the input image while incorporating elements from a text prompt.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional image generation models use text embeddings to generate images, then the images incorporate semantic elements from text prompts, but the models introduce biased depictions of individuals due to inherent correlations in text embeddings
Solution Approach 1:
The patent introduces an intermediary optimization process that mediates between the text embedding (which provides semantic content) and the identity features (which must be preserved). The optimization component acts as a mediator that adjusts the latent code to satisfy both the text prompt requirements and the identity preservation constraints, preventing direct harmful transfer of biased attributes while maintaining useful semantic integration.
Solution Approach 2:
The patent changes the parameter optimization approach by introducing identity-preserving loss functions that constrain the modification of identity-related parameters during the generation process. Instead of freely optimizing all parameters to match the text prompt, the system selectively constrains identity parameters (such as gender, ethnicity, age) while allowing other parameters to adapt to the text embedding, thus resolving the contradiction between adaptability and reliability.
2Reliability
If the model optimizes latent code to preserve identity, then biased depictions are reduced, but the complexity of the generation process increases
Solution Approach 1:
The patent segments the image generation process into distinct functional components: an image generation model that creates the preliminary image, an attribute classifier that identifies biased depictions, and an optimization component that adjusts the latent code. This segmentation allows each component to specialize in one task, making the overall complex system more manageable and efficient despite the increased number of components.
Solution Approach 2:
The patent implements a feedback loop where the attribute classifier evaluates the preliminary image for biased depictions and feeds this information back to the optimization component. The optimization component then adjusts the latent code based on this feedback to reduce biases while preserving identity. This feedback mechanism enables iterative refinement without requiring complete reprocessing, balancing complexity with effectiveness.
3Reliability
If the attribute classifier identifies biased depictions, then fairness is improved, but the processing time increases
Solution Approach 1:
The patent applies partial action by having the attribute classifier focus on detecting specific biased attributes (such as gender, ethnicity, age) rather than analyzing all possible image features. This selective detection approach reduces the computational burden and processing time while still effectively identifying and mitigating the most significant biases in the generated images.
Data Source
AI summary
A method, apparatus, non-transitory computer readable medium, apparatus, and system for image processing include obtaining a text prompt and an input image depicting a person, generating a latent code based on the text prompt and the input image, wherein the latent code is optimized by an identity preserving loss, and generating, using an image generator of a machine learning model, a synthetic image based on the latent code, wherein the synthetic image includes an element of the text prompt and preserves an identity of the person in the input image.