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 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

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

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

Psychiatric morbidity in older people with moderate and severe learning disability (mental retardation). Part I: development and reliability of the patient interview (the PAS-ADD)

This paper describes the development of the PAS-ADD, a semistructured clinical interview for use specifically with patients with learning disabilities, based on items drawn from the PSE. The PAS-ADD includes a number of novel features including: parallel interviewing of patient and informant; a three-tier structure to provide a flexible interview appropriate to the patient's intellectual level; use of a memorable 'anchor event' in the patient's life to improve time focus; and simplified wording, improved organisation and lay out. Inter-rater reliability was investigated using an experimental design in which two raters viewed and re-rated videotaped PAS-ADD interviews which had been conducted by an experienced clinician. Reliability results compared favourably with those obtained in a major study of PSE reliability with a sample drawn from non-learning disabled individuals. Mean kappa for all items was 0.72. Other indexes of reliability were also good. In the current phase of development, the PAS-ADD is to be expanded to include further diagnostic categories, including schizophrenia and autism. The new version will be updated for use with ICD-10 criteria

Comparison of ADCP observations and 3D model simulations of turbulence at a tidal energy site

Field measurement of turbulence in strong tidal currents is difficult and expensive, but the tidal energy industry needs to accurately quantify turbulence for adequate resource characterisation and device design. Models that can predict such turbulence could reduce measurement costs. We compare a Regional Ocean Modelling System (ROMS) simulation with acoustic Doppler current profiler (ADCP) measurements from a highly-energetic tidal site. This comparison shows the extent to which turbulence can be quantified by ROMS, using the conventional k−ε turbulence closure model. Both model and observations covered the same time period, encompassing two spring-neap cycles. Turbulent kinetic energy (TKE) density was calculated from measurements using the variance method; turbulent dissipation, ε, was calculated using the structure function method. Measurements show that wave action dominates turbulent fluctuations in the upper half of the water column; comparing results for deeper water, however, shows very strong agreement. A best fit between ROMS and ADCP results for mean velocity yields R2=0.98; for TKE, R2 is 0.84 when strongly wave-dominated times are excluded. Dissipation agrees less well: although time series of ε are well-correlated at similar depths, ROMS estimates a greater magnitude of dissipation than is measured, by a factor of up to 4.8

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