We propose an experimental method for measuring bias in face recognition...
CT scans are the standard-of-care for many clinical ailments, and are ne...
Perceptual metrics, like the Fréchet Inception Distance (FID), are widel...
Recent research demonstrates that deep learning models are capable of
pr...
Recent work demonstrates that images from various chest X-ray datasets
c...
Camera orientations (i.e., rotation and zoom) govern the content that a
...
Training dataset biases are by far the most scrutinized factors when
exp...
Implicit neural representations (INRs) have recently advanced numerous
v...
Algorithmic fairness is frequently motivated in terms of a trade-off in ...
We introduce a new neural signal representation designed for the efficie...
We present a method for finding paths in a deep generative model's laten...
Test-time augmentation (TTA)—the aggregation of predictions across
trans...
Measuring algorithmic bias is crucial both to assess algorithmic fairnes...
We introduce a new video synthesis task: synthesizing time lapse videos
...
We introduce visual deprojection: the task of recovering an image or vid...
Classical deformable registration techniques achieve impressive results ...
Biomedical image segmentation is an important task in many medical
appli...
Deformable registration of clinical scans is a fundamental task for many...
We present VoxelMorph, a fast, unsupervised, learning-based algorithm fo...
Traditional deformable registration techniques achieve impressive result...
We address the computational problem of novel human pose synthesis. Give...
We present an efficient learning-based algorithm for deformable, pairwis...
For many movement disorders, such as Parkinson's disease and ataxia, dis...