EigenGAN: Layer-Wise Eigen-Learning for GANs (2104.12476v2)

Abstract: Recent studies on Generative Adversarial Network (GAN) reveal that different layers of a generative CNN hold different semantics of the synthesized images. However, few GAN models have explicit dimensions to control the semantic attributes represented in a specific layer. This paper proposes EigenGAN which is able to unsupervisedly mine interpretable and controllable dimensions from different generator layers. Specifically, EigenGAN embeds one linear subspace with orthogonal basis into each generator layer. Via generative adversarial training to learn a target distribution, these layer-wise subspaces automatically discover a set of "eigen-dimensions" at each layer corresponding to a set of semantic attributes or interpretable variations. By traversing the coefficient of a specific eigen-dimension, the generator can produce samples with continuous changes corresponding to a specific semantic attribute. Taking the human face for example, EigenGAN can discover controllable dimensions for high-level concepts such as pose and gender in the subspace of deep layers, as well as low-level concepts such as hue and color in the subspace of shallow layers. Moreover, in the linear case, we theoretically prove that our algorithm derives the principal components as PCA does. Codes can be found in https://github.com/LynnHo/EigenGAN-Tensorflow.

EigenGAN: Layer-Wise Eigen-Learning for GANs

The paper "EigenGAN: Layer-Wise Eigen-Learning for GANs" presents an advanced approach to improving the interpretability and control of Generative Adversarial Networks (GANs) through a novel concept termed "eigen-learning." EigenGAN introduces a layer-wise integration of linear subspaces within a generative network's architecture, significantly enhancing its ability to discover and manipulate semantic attributes.

Core Contributions

The primary contribution of this research is the embedding of linear subspace models with orthogonal bases into the generator layers of GANs. Through adversarial training, these subspaces are capable of autonomously uncovering "eigen-dimensions," each representing distinct semantic attributes at various abstraction levels. This layer-wise eigen-learning offers significant advancements in enabling more interpretable and controllable generative processes.

Key findings show that deep layers of the generator are inclined to capture high-level semantic concepts, such as pose and gender, while shallower layers focus on low-level features like color and hue. Such insights align with previous understandings of CNNs, reinforcing the hypothesis regarding hierarchical representation in neural networks.

Numerical and Theoretical Strengths

The authors provide a theoretical basis for their approach by demonstrating that, for linear cases, EigenGAN is equivalent to deriving principal components using PCA. This theoretical foundation justifies the layer-wise subspace embedding as it distinctly separates principal variations in different network layers.

The experimental results reinforce these theoretical insights, revealing the model's capability to autonomously identify semantic attributes with high accuracy. For example, the model achieves an impressive level of dimensional disentanglement, demonstrated quantitatively through correlation measures with CelebA attributes.

Comparative Analysis

When compared with other state-of-the-art methods such as SeFa and GLD, EigenGAN shows comparable effectiveness in identifying and disentangling semantic attributes. Both qualitative and quantitative analyses suggest EigenGAN's robustness in producing interpretable and meaningful variations across various datasets such as CelebA, FFHQ, and others.

Implications and Future Directions

The implications of EigenGAN highlight its potential utility in applications requiring high interpretability, such as image editing and dataset augmentation. By enabling control over specific semantic dimensions, users can achieve precise modifications and explorations in the synthesized data.

The paper also opens avenues for further research into enhancing disentanglement techniques within GANs and exploring more advanced architectures. There is a suggestion for investigating supervised or semi-supervised approaches to reduce possible attribute entanglement within deeper layers.

Conclusion

EigenGAN emerges as a sophisticated tool for improving the interpretability and controllability of GANs by embedding linear subspaces in their layers. The theoretical and practical enhancements presented in this paper substantiate its value in both academic research and practical applications in AI. Future explorations could focus on reducing entanglements and expanding the applicability of such layer-wise learning strategies in broader contexts.