In machine learning, a generative model is responsible for generating new samples of data
in terms of a probabilistic model. Generative adversarial network (GAN) has been widely …

We systematically study a wide variety of generative models spanning semantically-diverse
image datasets to understand and improve the feature extractors and metrics used to …

We propose a learning-based method to recover normals, specularity, and roughness from a
single diffuse image of a material, using microgeometry appearance as our primary cue …

While standard evaluation scores for generative models are mostly reference-based, a
reference-dependent assessment of generative models could be generally difficult due to …

Selecting appropriate datasets is critical in modern computer vision. However, no general-
purpose tools exist to evaluate the extent to which two datasets differ. For this, we propose …

We propose a robust and reliable evaluation metric for generative models called
Topological Precision and Recall (TopP&R, pronounced “topper”), which systematically …

Abstract While recent Vision-Language (VL) models excel at open-vocabulary tasks it is
unclear how to use them with specific or uncommon concepts. Personalized Text-to-Image …

We present a novel approach for generating minority samples that live on low-density
regions of a data manifold. Our framework is built upon diffusion models, leveraging the …

Diffusion-based planning has shown promising results in long-horizon, sparse-reward tasks
by training trajectory diffusion models and conditioning the sampled trajectories using …

Knowledge distillation in neural networks refers to compressing a large model or dataset
into a smaller version of itself. We introduce Privacy Distillation, a framework that allows a …