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Found 4 papers, 3 papers with code
Defocus Blur Synthesis and Deblurring via Interpolation and Extrapolation in Latent Space
We train autoencoders with implicit and explicit regularization techniques to enforce linearity relations among the representations of different blur levels in the latent space.
RDA-INR: Riemannian Diffeomorphic Autoencoding via Implicit Neural Representations
In this work, we focus on a limitation of neural network-based atlas building and statistical latent modeling methods, namely that they either are (i) resolution dependent or (ii) disregard any data/problem-specific geometry needed for proper mean-variance analysis.
Generative modeling of living cells with SO(3)-equivariant implicit neural representations
Finally, we show how microscopy images of living cells that correspond to our generated cell shapes can be synthesized using an image-to-image model.
Implicit Neural Representations for Generative Modeling of Living Cell Shapes
Deep generative models for cell shape synthesis require a light-weight and flexible representation of the cell shape.
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