Search Results for author:
Found 10 papers, 8 papers with code
InAugment: Improving Classifiers via Internal Augmentation
Image augmentation techniques apply transformation functions such as rotation, shearing, or color distortion on an input image.
SWAGAN: A Style-based Wavelet-driven Generative Model
In recent years, considerable progress has been made in the visual quality of Generative Adversarial Networks (GANs).
Ranked #11 on
Image Generation
on FFHQ 1024 x 1024
SketchPatch: Sketch Stylization via Seamless Patch-level Synthesis
The paradigm of image-to-image translation is leveraged for the benefit of sketch stylization via transfer of geometric textural details.
MRGAN: Multi-Rooted 3D Shape Generation with Unsupervised Part Disentanglement
Our network encourages disentangled generation of semantic parts via two key ingredients: a root-mixing training strategy which helps decorrelate the different branches to facilitate disentanglement, and a set of loss terms designed with part disentanglement and shape semantics in mind.
Focus-and-Expand: Training Guidance Through Gradual Manipulation of Input Features
Drawing inspiration from Parameter Continuation methods, we propose steering the training process to consider specific features in the input more than others, through gradual shifts in the input domain.
Face Identity Disentanglement via Latent Space Mapping
Learning disentangled representations of data is a fundamental problem in artificial intelligence.
Masked Based Unsupervised Content Transfer
We consider the problem of translating, in an unsupervised manner, between two domains where one contains some additional information compared to the other.
Structural-analogy from a Single Image Pair
In this paper, we explore the capabilities of neural networks to understand image structure given only a single pair of images, A and B.
Unsupervised Multi-Modal Image Registration via Geometry Preserving Image-to-Image Translation
In this work, we bypass the difficulties of developing cross-modality similarity measures, by training an image-to-image translation network on the two input modalities.
Mask Based Unsupervised Content Transfer
We consider the problem of translating, in an unsupervised manner, between two domains where one contains some additional information compared to the other.
