Counterfactual generation with joint causal representation for generative adversarial networks

Area of Science:

• Computer Vision
• Artificial Intelligence
• Machine Learning

Background:

• Generative Adversarial Networks (GANs) often function as black boxes, limiting controllable image generation and semantic editing.
• Modifying multiple attributes in images generated by GANs remains a significant challenge.

Purpose of the Study:

• To develop a novel model, Counterfactual Generation with Joint Causal Representation (CG_JCR), for controllable image generation and semantic editing.
• To enable precise manipulation of multiple image attributes within GANs.

Main Methods:

• Utilized classifier gradients as prior knowledge to learn counterfactual joint semantic representations.
• Employed orthogonalization via continuous optimization to disentangle semantic representations.
• Developed independent counterfactual representation and disentanglement modules for pre-trained GANs.

Main Results:

• CG_JCR demonstrated superior performance compared to existing methods on the Celeba dataset.
• Intervention experiments and various metrics validated the model's effectiveness in generating and disentangling joint causal representations.
• The proposed method successfully addressed the limitations of black-box GANs in semantic editing.

Conclusions:

• CG_JCR provides an effective solution for controllable image generation and semantic editing in GANs.
• The model successfully disentangles joint causal representations, allowing for fine-grained attribute manipulation.
• Open-source code is available, facilitating further research and application.