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Found 11 papers, 4 papers with code
LATTE3D: Large-scale Amortized Text-To-Enhanced3D Synthesis
Recent text-to-3D generation approaches produce impressive 3D results but require time-consuming optimization that can take up to an hour per prompt.
TexFusion: Synthesizing 3D Textures with Text-Guided Image Diffusion Models
We present TexFusion (Texture Diffusion), a new method to synthesize textures for given 3D geometries, using large-scale text-guided image diffusion models.
Differentially Private Diffusion Models
While modern machine learning models rely on increasingly large training datasets, data is often limited in privacy-sensitive domains.
Scalable Neural Data Server: A Data Recommender for Transfer Learning
NDS uses a mixture of experts trained on data sources to estimate similarity between each source and the downstream task.
Don’t Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence
Generative models trained with privacy constraints on private data can sidestep this challenge, providing indirect access to private data instead.
Don't Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence
Generative models trained with privacy constraints on private data can sidestep this challenge, providing indirect access to private data instead.
Differentially Private Generative Models Through Optimal Transport
Generative models trained with privacy constraints on private data can sidestep this challenge and provide indirect access to the private data instead.
A Benchmark of Medical Out of Distribution Detection
However it is unclear which OoDD method should be used in practice.
Zero-Shot Compositional Policy Learning via Language Grounding
To facilitate the research on language-guided agents with domain adaption, we propose a novel zero-shot compositional policy learning task, where the environments are characterized as a composition of different attributes.
Which MOoD Methods work? A Benchmark of Medical Out of Distribution Detection
There is a rise in the use of deep learning for automated medical diagnosis, most notably in medical imaging.
A Theoretical Analysis of the Number of Shots in Few-Shot Learning
We introduce a theoretical analysis of the impact of the shot number on Prototypical Networks, a state-of-the-art few-shot classification method.
