Manifold Learning Benefits GANs
This work addresses a specific bottleneck in GANs for researchers and practitioners, offering an incremental improvement.
The paper tackles the problem of improving Generative Adversarial Networks by incorporating manifold learning into the discriminator, resulting in substantial outperformance over state-of-the-art baselines.
In this paper, we improve Generative Adversarial Networks by incorporating a manifold learning step into the discriminator. We consider locality-constrained linear and subspace-based manifolds, and locality-constrained non-linear manifolds. In our design, the manifold learning and coding steps are intertwined with layers of the discriminator, with the goal of attracting intermediate feature representations onto manifolds. We adaptively balance the discrepancy between feature representations and their manifold view, which is a trade-off between denoising on the manifold and refining the manifold. We find that locality-constrained non-linear manifolds outperform linear manifolds due to their non-uniform density and smoothness. We also substantially outperform state-of-the-art baselines.