Search Results for author:
Found 9 papers, 5 papers with code
"This is my unicorn, Fluffy": Personalizing frozen vision-language representations
We propose an architecture for solving PerVL that operates by extending the input vocabulary of a pretrained model with new word embeddings for the new personalized concepts.
State-of-the-Art in the Architecture, Methods and Applications of StyleGAN
Of these, StyleGAN offers a fascinating case study, owing to its remarkable visual quality and an ability to support a large array of downstream tasks.
Self-Conditioned Generative Adversarial Networks for Image Editing
We compare our models to a wide range of latent editing methods, and show that by alleviating the bias they achieve finer semantic control and better identity preservation through a wider range of transformations.
Stitch it in Time: GAN-Based Facial Editing of Real Videos
The ability of Generative Adversarial Networks to encode rich semantics within their latent space has been widely adopted for facial image editing.
HyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editing
In this work, we introduce this approach into the realm of encoder-based inversion.
StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators
Can a generative model be trained to produce images from a specific domain, guided by a text prompt only, without seeing any image?
LARGE: Latent-Based Regression through GAN Semantics
For modern generative frameworks, this semantic encoding manifests as smooth, linear directions which affect image attributes in a disentangled manner.
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).
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.
