43 citations · 44 across the 4 of their papers we have counts for
4 papers
Make-A-Texture: Fast Shape-Aware Texture Generation in 3 Seconds
Xiaoyu Xiang, Liat Sless Gorelik, Yuchen Fan +5
We present Make-A-Texture, a new framework that efficiently synthesizes high-resolution texture maps from textual prompts for given 3D geometries. Our approach progressively genera…
Grad-SAM: Explaining Transformers via Gradient Self-Attention Maps
Oren Barkan, Edan Hauon, Avi Caciularu +4
Transformer-based language models significantly advanced the state-of-the-art in many linguistic tasks. As this revolution continues, the ability to explain model predictions has b…
GAM: Explainable Visual Similarity and Classification via Gradient Activation Maps
Oren Barkan, Omri Armstrong, Amir Hertz +4
We present Gradient Activation Maps (GAM) - a machinery for explaining predictions made by visual similarity and classification models. By gleaning localized gradient and activatio…
Robust Model Compression Using Deep Hypotheses
Omri Armstrong, Ran Gilad-Bachrach
Machine Learning models should ideally be compact and robust. Compactness provides efficiency and comprehensibility whereas robustness provides resilience. Both topics have been st…