7 citations · 10 across the 4 of their papers we have counts for
5 papers
Discovering Interpretable Directions in the Semantic Latent Space of Diffusion Models
René Haas, Inbar Huberman-Spiegelglas, Rotem Mulayoff +3
Denoising Diffusion Models (DDMs) have emerged as a strong competitor to Generative Adversarial Networks (GANs). However, despite their widespread use in image synthesis and editin…
Controllable GAN Synthesis Using Non-Rigid Structure-from-Motion
René Haas, Stella Graßhof, Sami S. Brandt
In this paper, we present an approach for combining non-rigid structure-from-motion (NRSfM) with deep generative models,and propose an efficient framework for discovering trajector…
Tensor-based Emotion Editing in the StyleGAN Latent Space
René Haas, Stella Graßhof, Sami S. Brandt
In this paper, we use a tensor model based on the Higher-Order Singular Value Decomposition (HOSVD) to discover semantic directions in Generative Adversarial Networks. This is achi…
Tensor-based Subspace Factorization for StyleGAN
René Haas, Stella Graßhof, Sami Sebastian Brandt
In this paper, we propose GAN a tensor-based method for modeling the latent space of generative models. The objective is to identify semantic directions in latent space. To this…
Discriminating Between Similar Nordic Languages
René Haas, Leon Derczynski
Automatic language identification is a challenging problem. Discriminating between closely related languages is especially difficult. This paper presents a machine learning approac…