[Paper Review] Rethinking conditional GAN training: An approach using geometrically structured latent manifolds

Sameera Ramasinghe, Moshiur Farazi+3 more|arXiv (Cornell University)|Nov 25, 2020

Cell Image Analysis Techniques4 citations

TL;DR

This paper proposes a geometrically structured training approach for conditional GANs that enforces a bi-lipschitz mapping between the latent and output manifolds, significantly improving sample diversity and visual quality without architectural changes. By modifying only the training objective—specifically replacing the standard adversarial loss with a geometry-aware loss—its method, Pix2Pix-Geo, achieves state-of-the-art performance on image-to-image translation tasks while preserving manifold structure.

ABSTRACT

Conditional GANs (cGAN), in their rudimentary form, suffer from critical drawbacks such as the lack of diversity in generated outputs and distortion between the latent and output manifolds. Although efforts have been made to improve results, they can suffer from unpleasant side-effects such as the topology mismatch between latent and output spaces. In contrast, we tackle this problem from a geometrical perspective and propose a novel training mechanism that increases both the diversity and the visual quality of a vanilla cGAN, by systematically encouraging a bi-lipschitz mapping between the latent and the output manifolds. We validate the efficacy of our solution on a baseline cGAN (i.e., Pix2Pix) which lacks diversity, and show that by only modifying its training mechanism (i.e., with our proposed Pix2Pix-Geo), one can achieve more diverse and realistic outputs on a broad set of image-to-image translation tasks. Codes are available at https://github.com/samgregoost/Rethinking-CGANs.

Motivation & Objective

• To address the lack of diversity and manifold distortion in vanilla conditional GANs (cGANs), particularly in image-to-image translation tasks.
• To resolve the topology mismatch between latent and output manifolds, where Euclidean paths in the latent space do not correspond to geodesics on the generated manifold.
• To improve generator stochasticity beyond dropout by ensuring the generator genuinely leverages latent noise, not just random seeds.
• To eliminate the loss mismatch between adversarial and reconstruction objectives that compromises visual quality and diversity.
• To demonstrate that improved performance can be achieved through optimization-only modifications, not architectural complexity.

Proposed method

• Introduce a novel training objective that enforces a bi-lipschitz mapping between the latent space and the output manifold, preserving geometric structure.
• Replace the standard adversarial loss in cGANs with a geometry-aware loss that aligns Euclidean paths in the latent space with geodesics on the output manifold.
• Use a modified objective combining a feature reconstruction loss, a style loss, and a total variation loss to stabilize training while preserving geometric fidelity.
• Apply the method to the baseline Pix2Pix model, creating a new variant called Pix2Pix-Geo, which only modifies the training procedure, not the architecture.
• Enforce continuity and bijectivity between latent and output manifolds via optimization, ensuring that latent space manipulations yield meaningful, smooth interpolations.
• Validate the approach across diverse image-to-image translation tasks, including sketch-to-faces, edge-to-shoes, and segmentation-to-faces, using qualitative and quantitative evaluation.

Experimental results

Research questions

• RQ1 Can a geometrically structured latent manifold improve diversity and visual quality in conditional GANs without architectural changes?
• RQ2 Does enforcing a bi-lipschitz mapping between latent and output manifolds reduce topology mismatch and improve interpolation quality?
• RQ3 Can the loss mismatch between adversarial and reconstruction objectives be mitigated through a geometry-aware training objective?
• RQ4 Does the proposed method outperform more complex state-of-the-art models on image-to-image translation benchmarks?
• RQ5 Can the generator be made to meaningfully utilize latent noise, rather than relying solely on dropout for stochasticity?

Key findings

• Pix2Pix-Geo achieves significantly higher output diversity than the original Pix2Pix, even though only the training procedure is modified.
• The model produces smoother and more meaningful interpolations between latent codes, indicating improved geometric structure in the latent space.
• Visual quality is enhanced, with generated images showing fewer artifacts and better alignment with input conditions.
• On multiple image-to-image translation tasks—including sketch-to-faces, sketch-to-shoes, and labels-to-facades—Pix2Pix-Geo matches or exceeds the performance of more complex state-of-the-art models.
• The method successfully reduces the reliance on dropout for stochasticity, as the generator now genuinely leverages latent noise for diverse outputs.
• The approach is generic and applicable to any vanilla cGAN, as demonstrated by its successful adaptation to segmentation-to-faces and edge-to-faces tasks.