1 citations · 1 across the 6 of their papers we have counts for
7 papers
CtrlSpeech: Coarse-to-Fine Control for Expressive Speech Synthesis
Zhisheng Zheng, Xiaohang Sun, Zhu Liu +5
Recent Text-To-Speech (TTS) systems have achieved strong naturalness and zero-shot voice cloning performance, but fine-grained control of expressive speech at the word or phoneme l…
Group-Aware Reinforcement Learning for Output Diversity in Large Language Models
Oron Anschel, Alon Shoshan, Adam Botach +7
Large Language Models (LLMs) often suffer from mode collapse, repeatedly generating the same few completions even when many valid answers exist, limiting their diversity across a w…
RosettaSpeech: Zero-Shot Speech-to-Speech Translation without Parallel Speech
Zhisheng Zheng, Xiaohang Sun, Tuan Dinh +8
End-to-end speech-to-speech translation (S2ST) systems typically struggle with a critical data bottleneck: the scarcity of parallel speech-to-speech corpora. To overcome this, we i…
LV-MAE: Learning Long Video Representations through Masked-Embedding Autoencoders
Ilan Naiman, Emanuel Ben-Baruch, Oron Anschel +4
In this work, we introduce long-video masked-embedding autoencoders (LV-MAE), a self-supervised learning framework for long video representation. Our approach treats short- and lon…
Data Pruning via Separability, Integrity, and Model Uncertainty-Aware Importance Sampling
Steven Grosz, Rui Zhao, Rajeev Ranjan +4
This paper improves upon existing data pruning methods for image classification by introducing a novel pruning metric and pruning procedure based on importance sampling. The propos…
Distilling the Knowledge in Data Pruning
Emanuel Ben-Baruch, Adam Botach, Igor Kviatkovsky +2
With the increasing size of datasets used for training neural networks, data pruning becomes an attractive field of research. However, most current data pruning algorithms are limi…
FPGAN-Control: A Controllable Fingerprint Generator for Training with Synthetic Data
Alon Shoshan, Nadav Bhonker, Emanuel Ben Baruch +5
Training fingerprint recognition models using synthetic data has recently gained increased attention in the biometric community as it alleviates the dependency on sensitive persona…