Recently developed technologies to generate single-cell genomic data have made a revolutionary impact in the field of biology.
We introduce an inversion based method, denoted as IMAge-Guided model INvErsion (IMAGINE), to generate high-quality and diverse images from only a single training sample.
Training generative models, such as GANs, on a target domain containing limited examples (e. g., 10) can easily result in overfitting.
Extensive research in neural style transfer methods has shown that the correlation between features extracted by a pre-trained VGG network has a remarkable ability to capture the visual style of an image.
Identifying the underlying structures of a PDE system based upon a small set of data samples on the solution space is challenging for machine learning.
Caricature is an artistic drawing created to abstract or exaggerate facial features of a person.
People often create art by following an artistic workflow involving multiple stages that inform the overall design.
In this work, we present a new knowledge distillation method (named Collaborative Distillation) for encoder-decoder based neural style transfer to reduce the convolutional filters.
Existing video prediction methods mainly rely on observing multiple historical frames or focus on predicting the next one-frame.
Photorealistic image stylization concerns transferring style of a reference photo to a content photo with the constraint that the stylized photo should remain photorealistic.
In contrast to existing methods that consider only the guidance image, the proposed algorithm can selectively transfer salient structures that are consistent with both guidance and target images.
The whitening and coloring transforms reflect a direct matching of feature covariance of the content image to a given style image, which shares similar spirits with the optimization of Gram matrix based cost in neural style transfer.
Recent progresses on deep discriminative and generative modeling have shown promising results on texture synthesis.
A popular solution is upsampling the obtained noisy low resolution depth map with the guidance of the companion high resolution color image.