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Found 9 papers, 5 papers with code
Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
In order to create a personalized talking head model, these works require training on a large dataset of images of a single person.
Fast Bi-layer Neural Synthesis of One-Shot Realistic Head Avatars
The texture image is generated offline, warped and added to the coarse image to ensure a high effective resolution of synthesized head views.
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification
In this paper we push this Pareto frontier in the few-shot image classification setting with a key contribution: a new adaptive block called Contextual Squeeze-and-Excitation (CaSE) that adjusts a pretrained neural network on a new task to significantly improve performance with a single forward pass of the user data (context).
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Few-Shot Image Classification
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FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification
Modern deep learning systems are increasingly deployed in situations such as personalization and federated learning where it is necessary to support i) learning on small amounts of data, and ii) communication efficient distributed training protocols.
On the Efficacy of Differentially Private Few-shot Image Classification
There has been significant recent progress in training differentially private (DP) models which achieve accuracy that approaches the best non-private models.
Textured Neural Avatars
In particular, our system estimates an explicit two-dimensional texture map of the model surface.
Diffusion-Augmented Neural Processes
Over the last few years, Neural Processes have become a useful modelling tool in many application areas, such as healthcare and climate sciences, in which data are scarce and prediction uncertainty estimates are indispensable.
Transformer Neural Autoregressive Flows
In this paper, we propose a novel solution to these challenges by exploiting transformers to define a new class of neural flows called Transformer Neural Autoregressive Flows (T-NAFs).
Denoising Diffusion Probabilistic Models in Six Simple Steps
Denoising Diffusion Probabilistic Models (DDPMs) are a very popular class of deep generative model that have been successfully applied to a diverse range of problems including image and video generation, protein and material synthesis, weather forecasting, and neural surrogates of partial differential equations.
