output
20162026
most citedGAN Augmentation: Augmenting Training Data using Generative Adversarial Networks

353 citations

Showing 2024Show all

7 papers · 1 filter

cs.LG2024★ 1 cited

MultiTASC++: A Continuously Adaptive Scheduler for Edge-Based Multi-Device Cascade Inference

Sokratis Nikolaidis, Stylianos I. Venieris, Iakovos S. Venieris

Cascade systems, consisting of a lightweight model processing all samples and a heavier, high-accuracy model refining challenging samples, have become a widely-adopted distributed…

eess.AS2024★ 2 cited

Speech Boosting: Low-Latency Live Speech Enhancement for TWS Earbuds

Hanbin Bae, Pavel Andreev, Azat Saginbaev +4

This paper introduces a speech enhancement solution tailored for true wireless stereo (TWS) earbuds on-device usage. The solution was specifically designed to support conversations…

cs.LG2024

An Analysis of Linear Complexity Attention Substitutes with BEST-RQ

Ryan Whetten, Titouan Parcollet, Adel Moumen +2

Self-Supervised Learning (SSL) has proven to be effective in various domains, including speech processing. However, SSL is computationally and memory expensive. This is in part due…

cs.LG2024★ 4 cited

CARIn: Constraint-Aware and Responsive Inference on Heterogeneous Devices for Single- and Multi-DNN Workloads

Ioannis Panopoulos, Stylianos I. Venieris, Iakovos S. Venieris

The relentless expansion of deep learning applications in recent years has prompted a pivotal shift toward on-device execution, driven by the urgent need for real-time processing,…

eess.AS2024

Analyzing Speech Unit Selection for Textless Speech-to-Speech Translation

Jarod Duret, Yannick Estève, Titouan Parcollet

Recent advancements in textless speech-to-speech translation systems have been driven by the adoption of self-supervised learning techniques. Although most state-of-the-art systems…

cs.CV2024★ 2 cited

A Modular System for Enhanced Robustness of Multimedia Understanding Networks via Deep Parametric Estimation

Francesco Barbato, Umberto Michieli, Mehmet Kerim Yucel +2

In multimedia understanding tasks, corrupted samples pose a critical challenge, because when fed to machine learning models they lead to performance degradation. In the past, three…