10 papers
Gefen: Optimized Stochastic Optimizer
Nadav Benedek, Tomer Koren, Ohad Fried
AdamW is a default optimizer for modern deep learning, but its first and second moment states add roughly two parameter-sized buffers to training memory, increasing the already sub…
NIV: Neural Axis Variations for Variable Font Generation
Nadav Benedek, Ariel Shamir, Ohad Fried
Variable fonts enable continuous variation of glyph geometry along semantic design axes such as weight, width, slant, and optical size. However, constructing a variable font from a…
Optimal Transport Flow Matching by Design
Shimon Malnick, Matan Rusanovsky, Ohad Fried +1
Flow matching models learn to transport samples from a simple prior distribution to a complex data distribution. When prior-data pairs are coupled via optimal transport (OT), the l…
Exploring and Exploiting Stability in Latent Flow Matching
Rania Briq, Michael Kamp, Ohad Fried +2
In this work, we show that Latent Flow-Matching (LFM) models are robust to different types of perturbations, including data reduction and model capacity shrinkage. We characterize…
The Amazing Stability of Flow Matching
Rania Briq, Michael Kamp, Ohad Fried +2
The success of deep generative models in generating high-quality and diverse samples is often attributed to particular architectures and large training datasets. In this paper, we…
Copy-Trasform-Paste: Zero-Shot Object-Object Alignment Guided by Vision-Language and Geometric Constraints
Rotem Gatenyo, Ohad Fried
We study zero-shot 3D alignment of two given meshes, using a text prompt describing their spatial relation -- an essential capability for content creation and scene assembly. Earli…