5 papers
FreeSliders: Training-Free, Modality-Agnostic Concept Sliders for Fine-Grained Diffusion Control in Images, Audio, and Video
Rotem Ezra, Hedi Zisling, Nimrod Berman +5
Diffusion models have become state-of-the-art generative models for images, audio, and video, yet enabling fine-grained controllable generation, i.e., continuously steering specifi…
DiffSDA: Unsupervised Diffusion Sequential Disentanglement Across Modalities
Hedi Zisling, Ilan Naiman, Nimrod Berman +2
Unsupervised representation learning, particularly sequential disentanglement, aims to separate static and dynamic factors of variation in data without relying on labels. This rema…
One-Step Offline Distillation of Diffusion-based Models via Koopman Modeling
Nimrod Berman, Ilan Naiman, Moshe Eliasof +2
Diffusion-based generative models have demonstrated exceptional performance, yet their iterative sampling procedures remain computationally expensive. A prominent strategy to mitig…
A Multi-Task Learning Approach to Linear Multivariate Forecasting
Liran Nochumsohn, Hedi Zisling, Omri Azencot
Accurate forecasting of multivariate time series data is important in many engineering and scientific applications. Recent state-of-the-art works ignore the inter-relations between…
VNT-Net: Rotational Invariant Vector Neuron Transformers
Hedi Zisling, Andrei Sharf
Learning 3D point sets with rotational invariance is an important and challenging problem in machine learning. Through rotational invariant architectures, 3D point cloud neural net…