GAN Encoder Latent Space Regularization for Image Artefact Reduction
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Solution Overview
Problem
Generative Adversarial Networks (GANs) often produce images with undesirable artefacts such as distortions and unnatural structures, which hampers their ability to generate realistic and artefact-free data.
Innovation Solution
The introduction of an encoder into the GAN architecture, leveraging the Lipschitz continuity condition, allows for latent space regularizations. This enables the encoder to minimize the latent space differences between generated and target samples, thereby reducing artefacts in generated images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If GANs are used to generate images, then new data can be generated, but undesirable artefacts such as distortions and unnatural structures appear in the generated images
Solution Approach 1:
The patent introduces an encoder as an intermediary component between the generator and the latent space. The encoder maps generated images to latent space representations, enabling regularization constraints to be applied. This intermediary structure allows the system to maintain the generative capability of GANs while adding control mechanisms to reduce artefacts and improve image quality through latent space regularization.
2Manufacturing precision
If latent space regularizations are applied to reduce artefacts, then image quality improves, but the complexity of the GAN architecture increases
Solution Approach 1:
The patent transitions the problem from image space to latent space by introducing an encoder that maps images to latent representations. This dimensionality change allows regularization to be applied in the latent space rather than directly in image space. The encoder-GAN framework operates in two dimensions: the generator works in image space while the encoder regularizes in latent space, resolving the contradiction by solving the quality issue in a different dimensional domain.
Data Source
AI summary
A method for image generation based on a Generative AI Network. The Generative AI Network includes a generator and an encoder. The method includes determining, by the encoder, a first encoding E(Y) of a target image Y; generating, by the generator, a generated image G(Z) corresponding to the target image Y, wherein the generated image G(Z) is located in a close vicinity of a target neighborhood of the target image Y, and outputs of the generator are mapped, by the encoder, to a latent space adaptable to manipulate at least one characteristics of images generated by the Generative AI Network; and generating, by the encoder, a second encoding E(G(Z)) of the generated image G(Z) corresponding to the target image Y, wherein the first and second encodings E(Y) and E(G(Z)) map the target image Y and the generated image G(Z) to the latent space.