Image Translation Model Debiasing for Minority Attribute Generation
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Solution Overview
Problem
Image translation models, such as GANs, exhibit bias towards majority attributes due to underrepresentation of minority attributes in training datasets, leading to poor generation of images with minority attributes.
Innovation Solution
The model is trained using a balanced batch of images with oversampling of minority attributes, applying supervised contrastive loss to separate latent representations in the latent space, and an auxiliary classifier loss to enhance attribute prediction, thereby debiasing the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard training data is used without oversampling, then training data processing is simple, but the model generates bias towards majority attributes and fails to generate images with minority attributes
Solution Approach 1:
The patent applies preliminary action by creating a balanced batch of images before training the image translation model. Minority attribute images are oversampled in advance to ensure equal representation with majority attribute images, preventing bias during the training process and enabling accurate generation of minority attribute images.
Solution Approach 2:
The patent changes the parameter of data distribution by transforming the original imbalanced training data into a balanced batch through oversampling. This parameter change ensures that the model receives equal representation of minority and majority attributes during training, fundamentally altering the learning dynamics to reduce bias.
2Measurement precision
If supervised contrastive loss is applied to separate latent representations, then attribute separation in latent space improves, but training computational cost increases
Solution Approach 1:
The patent introduces an intermediary mechanism by applying supervised contrastive loss as an additional training objective that mediates between the image translation task and the attribute separation goal. This intermediary loss function explicitly encourages the encoder to separate latent representations of different attributes while maintaining the primary translation functionality.
Solution Approach 2:
The patent segments the training objective into multiple components: the primary image translation loss and the additional supervised contrastive loss for attribute separation. This segmentation allows the model to focus on both translation accuracy and attribute differentiation simultaneously, with each loss function handling a specific aspect of the problem.
3Measurement precision
If auxiliary classifier loss is applied to enhance attribute prediction, then minority attribute prediction accuracy improves, but training time increases
Solution Approach 1:
The patent applies multi-functionality by introducing an auxiliary classifier that serves multiple purposes: it predicts minority attributes during training to guide the contrastive loss, and can potentially be used for attribute prediction during inference. This single component performs both training guidance and potential inference functions, reducing the need for separate specialized components.
Data Source
AI summary
A system debiases image translation models to produce generated images that contain minority attributes. A balanced batch for a minority attribute is created by over-sampling images having the minority attribute from an image dataset. An image translation model is trained using images from the balanced batch by applying supervised contrastive loss to output of an encoder of the image translation model and an auxiliary classifier loss based on predicted attributes in images generated by a decoder of the image translation model. Once trained, the image translation model is used to generate images with the minority image when given an input image having the minority attribute.