3 accepted papers
Diffusion models have shown tremendous results in image generation. However due to the iterative nature of the diffusion process and its reliance on classifier-free guidance inference times are slow. In this paper we propose a new distillation approach for guided diffusion models in which an externa…
Several recently proposed unsupervised meta-learning approaches rely on synthetic meta-tasks created using techniques such as random selection, clustering and/or augmentation. In this work, we describe a novel approach that generates meta-tasks using generative models. The proposed family of algorit…
Few-shot or one-shot learning of classifiers requires a significant inductive bias towards the type of task to be learned. One way to acquire this is by meta-learning on tasks similar to the target task. In this paper, we propose UMTRA, an algorithm that performs unsupervised, model-agnostic meta-le…