55 research outputs found
The GAN that Warped: Semantic Attribute Editing with Unpaired Data
Deep neural networks have recently been used to edit images with great success, in particular for faces.However, they are often limited to only being able to work at a restricted range of resolutions. Many methods are so flexible that face edits can often result in an unwanted loss of identity.This work proposes to learn how to perform semantic image edits through the application of smooth warp fields.Previous approaches that attempted to use warping for semantic edits required paired data, \ie example images of the same subject with different semantic attributes.In contrast, we employ recent advances in Generative Adversarial Networks that allow our model to be trained with unpaired data.We demonstrate face editing at very high resolutions (4k images) with a single forward pass of a deep network at a lower resolution.We also show that our edits are substantially better at preserving the subject's identity.The robustness of our approach is demonstrated by showing plausible image editing results on the Cub200 birds dataset.To our knowledge this has not been previously accomplished, due the challenging nature of the dataset
Structured Uncertainty Prediction Networks
This paper is the first work to propose a network to predict a structured uncertainty distribution for a synthesized image.Previous approaches have been mostly limited to predicting diagonal covariance matrices.Our novel model learns to predict a full Gaussian covariance matrix for each reconstruction, which permits efficient sampling and likelihood evaluation.We demonstrate that our model can accurately reconstruct ground truth correlated residual distributions for synthetic datasets and generate plausible high frequency samples for real face images. We also illustrate the use of these predicted covariances for structure preserving image denoising.<br/
The GAN that Warped: Semantic Attribute Editing with Unpaired Data
Deep neural networks have recently been used to edit images with great
success, in particular for faces. However, they are often limited to only being
able to work at a restricted range of resolutions. Many methods are so flexible
that face edits can often result in an unwanted loss of identity. This work
proposes to learn how to perform semantic image edits through the application
of smooth warp fields. Previous approaches that attempted to use warping for
semantic edits required paired data, i.e. example images of the same subject
with different semantic attributes. In contrast, we employ recent advances in
Generative Adversarial Networks that allow our model to be trained with
unpaired data. We demonstrate face editing at very high resolutions (4k images)
with a single forward pass of a deep network at a lower resolution. We also
show that our edits are substantially better at preserving the subject's
identity
Laplacian Pyramid of Conditional Variational Autoencoders
Variational Autoencoders (VAE) learn a latent representation of image data that allows natural image generation and manipulation. However, they struggle to generate sharp images.To address this problem, we propose a hierarchy of VAEs analogous to a Laplacian pyramid. Each network models a single pyramid level, and is conditioned on the coarser levels. The Laplacian architecture allows for novel image editing applications that take advantage of the coarse to fine structure of the model. Our method achieves lower reconstruction error in terms of MSE, which is the loss function of the VAE and is not directly minimised in our model. Furthermore, the reconstructions generated by the proposed model are preferred over those from the VAE by human evaluators
Structured uncertainty prediction networks
This paper is the first work to propose a network to predict a structured uncertainty distribution for a synthesized image. Previous approaches have been mostly limited to predicting diagonal covariance matrices. Our novel model learns to predict a full Gaussian covariance matrix for each reconstruction, which permits efficient sampling and likelihood evaluation.
We demonstrate that our model can accurately reconstruct ground truth correlated residual distributions for synthetic datasets and generate plausible high frequency samples for real face images. We also illustrate the use of these predicted covariances for structure preserving image denoising
The GAN that warped: semantic attribute editing with unpaired data
Deep neural networks have recently been used to edit images with great success, in particular for faces. However, they are often limited to only being able to work at a restricted range of resolutions. Many methods are so flexible that face edits can often result in an unwanted loss of identity. This work proposes to learn how to perform semantic image edits through the application of smooth warp fields. Previous approaches that attempted to use warping for semantic edits required paired data, i.e. example images of the same subject with different semantic attributes. In contrast, we employ recent advances in Generative Adversarial Networks that allow our model to be trained with unpaired data. We demonstrate face editing at very high resolutions (4k images) with a single forward pass of a deep network at a lower resolution. We also show that our edits are substantially better at preserving the subject's identity. The robustness of our approach is demonstrated by showing plausible image editing results on the Cub200 birds dataset. To our knowledge this has not been previously accomplished, due the challenging nature of the dataset
Compressed Sensing MRI Reconstruction Regularized by VAEs with Structured Image Covariance
Objective: This paper investigates how generative models, trained on
ground-truth images, can be used \changes{as} priors for inverse problems,
penalizing reconstructions far from images the generator can produce. The aim
is that learned regularization will provide complex data-driven priors to
inverse problems while still retaining the control and insight of a variational
regularization method. Moreover, unsupervised learning, without paired training
data, allows the learned regularizer to remain flexible to changes in the
forward problem such as noise level, sampling pattern or coil sensitivities in
MRI.
Approach: We utilize variational autoencoders (VAEs) that generate not only
an image but also a covariance uncertainty matrix for each image. The
covariance can model changing uncertainty dependencies caused by structure in
the image, such as edges or objects, and provides a new distance metric from
the manifold of learned images.
Main results: We evaluate these novel generative regularizers on
retrospectively sub-sampled real-valued MRI measurements from the fastMRI
dataset. We compare our proposed learned regularization against other unlearned
regularization approaches and unsupervised and supervised deep learning
methods.
Significance: Our results show that the proposed method is competitive with
other state-of-the-art methods and behaves consistently with changing sampling
patterns and noise levels
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