InterFaceGAN: Interpreting the Disentangled Face Representation Learned by GANs (2005.09635v2)

Abstract: Although Generative Adversarial Networks (GANs) have made significant progress in face synthesis, there lacks enough understanding of what GANs have learned in the latent representation to map a random code to a photo-realistic image. In this work, we propose a framework called InterFaceGAN to interpret the disentangled face representation learned by the state-of-the-art GAN models and study the properties of the facial semantics encoded in the latent space. We first find that GANs learn various semantics in some linear subspaces of the latent space. After identifying these subspaces, we can realistically manipulate the corresponding facial attributes without retraining the model. We then conduct a detailed study on the correlation between different semantics and manage to better disentangle them via subspace projection, resulting in more precise control of the attribute manipulation. Besides manipulating the gender, age, expression, and presence of eyeglasses, we can even alter the face pose and fix the artifacts accidentally made by GANs. Furthermore, we perform an in-depth face identity analysis and a layer-wise analysis to evaluate the editing results quantitatively. Finally, we apply our approach to real face editing by employing GAN inversion approaches and explicitly training feed-forward models based on the synthetic data established by InterFaceGAN. Extensive experimental results suggest that learning to synthesize faces spontaneously brings a disentangled and controllable face representation.

InterFaceGAN: Interpreting the Disentangled Face Representation Learned by GANs

The paper "InterFaceGAN: Interpreting the Disentangled Face Representation Learned by GANs" presents a framework aimed at understanding and manipulating the latent space representations in Generative Adversarial Networks (GANs), particularly focusing on face synthesis tasks. This work addresses a significant gap in the current understanding of how GANs capture and encode various facial semantics within the latent space.

Summary of Contributions

The authors introduce InterFaceGAN, a framework designed to interpret and manipulate the latent representations learned by state-of-the-art GANs. The core idea is to identify linear subspaces in the latent space that correspond to specific facial attributes without requiring model retraining. This allows for realistic and controlled manipulation of these attributes, such as age, gender, expression, eyeglasses, and pose.

Methodology

InterFaceGAN employs off-the-shelf classifiers to predict semantic scores for synthesized images, acting as a bridge between the latent space and semantic attributes. This approach reveals that binary attributes align with linear subspaces in the latent space. By identifying these subspaces, direct manipulation of corresponding facial attributes becomes feasible.

The authors also propose a method for disentangling correlated attributes using subspace projection, achieving more precise control over attribute manipulation. Additionally, they conduct an extensive face identity analysis and a layer-wise analysis to evaluate editing results, providing a quantitative measure of the framework's effectiveness.

Key Findings

• Linear Separability: The latent space includes hyperplanes that cleanly separate different facial attributes. Experiments show over 95% classification accuracy on validation sets using these hyperplanes.
• Attribute Manipulation: By varying the latent codes along identified subspaces, diverse attributes can be manipulated effectively. For example, altering pose or adding eyeglasses to a face without retraining the GAN model.
• Disentanglement: Empirical results suggest that the latent space of GANs can be effectively disentangled to control correlated attributes. For instance, controlling age enhancement without altering gender.

Results and Implications

The framework's application in face editing demonstrates its potential for practical image manipulation tasks. The disentangled representation allows for precise and realistic attribute editing, which is critical for applications in fields such as facial recognition and aesthetic modification.

The use of layer-wise analysis provides insights into how GANs learn various features across different network layers. This understanding could guide future endeavors in designing more interpretable and controllable generative models.

Future Directions

The authors highlight several directions for future work:

• Exploring more complex scenes and objects beyond facial attributes using InterFaceGAN’s methodology.
• Addressing limitations related to long-distance manipulations within the latent space, potentially through non-linear models.
• Enhancing unsupervised learning techniques to identify more intricate or emergent semantics within GANs.

Conclusion

InterFaceGAN stands as a significant contribution towards understanding and leveraging the latent space of GANs. By providing a means to interpret and manipulate facial attributes directly from the latent space, this work opens avenues for both theoretical exploration and practical application in visual synthesis and editing tasks. The methodology proposed offers a blueprint for extending similar analysis to other GAN-based models, impacting various domains within artificial intelligence and computer vision research.