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Found 8 papers, 1 papers with code
Score-based pullback Riemannian geometry
Data-driven Riemannian geometry has emerged as a powerful tool for interpretable representation learning, offering improved efficiency in downstream tasks.
Variational Diffusion Auto-encoder: Latent Space Extraction from Pre-trained Diffusion Models
This issue stems from the unrealistic assumption that approximates the conditional data distribution, $p(\textbf{x} | \textbf{z})$, as an isotropic Gaussian.
Your diffusion model secretly knows the dimension of the data manifold
A diffusion model approximates the score function i. e. the gradient of the log density of a noise-corrupted version of the target distribution for varying levels of corruption.
Non-Uniform Diffusion Models
We show that non-uniform diffusion leads to multi-scale diffusion models which have similar structure to this of multi-scale normalizing flows.
Conditional Image Generation with Score-Based Diffusion Models
Score-based diffusion models have emerged as one of the most promising frameworks for deep generative modelling.
CAFLOW: Conditional Autoregressive Flows
We model the conditional distribution of the latent encodings by modeling the auto-regressive distributions with an efficient multi-scale normalizing flow, where each conditioning factor affects image synthesis at its respective resolution scale.
How to distribute data across tasks for meta-learning?
We show that: 1) If tasks are homogeneous, there is a uniform optimal allocation, whereby all tasks get the same amount of data; 2) At fixed budget, there is a trade-off between number of tasks and number of data points per task, with a unique solution for the optimum; 3) When trained separately, harder task should get more data, at the cost of a smaller number of tasks; 4) When training on a mixture of easy and hard tasks, more data should be allocated to easy tasks.
Optimal allocation of data across training tasks in meta-learning
They are tested on benchmarks with a fixed number of data-points for each training task, and this number is usually arbitrary, for example, 5 instances per class in few-shot classification.
