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Found 13 papers, 2 papers with code
Playground v2.5: Three Insights towards Enhancing Aesthetic Quality in Text-to-Image Generation
In this work, we share three insights for achieving state-of-the-art aesthetic quality in text-to-image generative models.
RadImageGAN -- A Multi-modal Dataset-Scale Generative AI for Medical Imaging
We showed that using synthetic auto-labeled data from RadImageGAN can significantly improve performance on four diverse downstream segmentation datasets by augmenting real training data and/or developing pre-trained weights for fine-tuning.
DreamTeacher: Pretraining Image Backbones with Deep Generative Models
In this work, we introduce a self-supervised feature representation learning framework DreamTeacher that utilizes generative networks for pre-training downstream image backbones.
NeuralField-LDM: Scene Generation with Hierarchical Latent Diffusion Models
We first train a scene auto-encoder to express a set of image and pose pairs as a neural field, represented as density and feature voxel grids that can be projected to produce novel views of the scene.
GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images
As several industries are moving towards modeling massive 3D virtual worlds, the need for content creation tools that can scale in terms of the quantity, quality, and diversity of 3D content is becoming evident.
How Much More Data Do I Need? Estimating Requirements for Downstream Tasks
Given a small training data set and a learning algorithm, how much more data is necessary to reach a target validation or test performance?
Polymorphic-GAN: Generating Aligned Samples across Multiple Domains with Learned Morph Maps
Modern image generative models show remarkable sample quality when trained on a single domain or class of objects.
Generative Adversarial Network
Image-to-Image Translation
+1
BigDatasetGAN: Synthesizing ImageNet with Pixel-wise Annotations
By training an effective feature segmentation architecture on top of BigGAN, we turn BigGAN into a labeled dataset generator.
EditGAN: High-Precision Semantic Image Editing
EditGAN builds on a GAN framework that jointly models images and their semantic segmentations, requiring only a handful of labeled examples, making it a scalable tool for editing.
Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization
Training deep networks with limited labeled data while achieving a strong generalization ability is key in the quest to reduce human annotation efforts.
Fed-Sim: Federated Simulation for Medical Imaging
Since the model of geometry and material is disentangled from the imaging sensor, it can effectively be trained across multiple medical centers.
Neural Turtle Graphics for Modeling City Road Layouts
We propose Neural Turtle Graphics (NTG), a novel generative model for spatial graphs, and demonstrate its applications in modeling city road layouts.
A Face-to-Face Neural Conversation Model
The decoder consists of two layers, where the lower layer aims at generating the verbal response and coarse facial expressions, while the second layer fills in the subtle gestures, making the generated output more smooth and natural.
