8 papers
Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance
Gal Vinograd, Idan Achituve, Ethan Fetaya
We present EDDY (Exact-marginal Diversification via Divergence-free dYnamics), a guidance mechanism for diffusion and flow matching models that promotes diversity among samples gen…
LATMiX: Learnable Affine Transformations for Microscaling Quantization of LLMs
Ofir Gordon, Lior Dikstein, Arnon Netzer +2
Post-training quantization (PTQ) is a widely used approach for reducing the memory and compute costs of large language models (LLMs). Recent studies have shown that applying invert…
Efficient Image Restoration via Latent Consistency Flow Matching
Elad Cohen, Idan Achituve, Idit Diamant +2
Recent advances in generative image restoration (IR) have demonstrated impressive results. However, these methods are hindered by their substantial size and computational demands,…
Inverse Problem Sampling in Latent Space Using Sequential Monte Carlo
Idan Achituve, Hai Victor Habi, Amir Rosenfeld +3
In image processing, solving inverse problems is the task of finding plausible reconstructions of an image that was corrupted by some (usually known) degradation operator. Commonly…
Few-Shot Speech Deepfake Detection Adaptation with Gaussian Processes
Neta Glazer, David Chernin, Idan Achituve +2
Recent advancements in Text-to-Speech (TTS) models, particularly in voice cloning, have intensified the demand for adaptable and efficient deepfake detection methods. As TTS system…
De-Confusing Pseudo-Labels in Source-Free Domain Adaptation
Idit Diamant, Amir Rosenfeld, Idan Achituve +2
Source-free domain adaptation aims to adapt a source-trained model to an unlabeled target domain without access to the source data. It has attracted growing attention in recent yea…