Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Kersner, Martin"'
Improving the performance of on-device audio classification models remains a challenge given the computational limits of the mobile environment. Many studies leverage knowledge distillation to boost predictive performance by transferring the knowledg
Externí odkaz:
http://arxiv.org/abs/2110.14131
When there is a mismatch between the target identity and the driver identity, face reenactment suffers severe degradation in the quality of the result, especially in a few-shot setting. The identity preservation problem, where the model loses the det
Externí odkaz:
http://arxiv.org/abs/1911.08139
Autor:
Seo, Seokjun, Choi, Seungwoo, Kersner, Martin, Shin, Beomjun, Yoon, Hyungsuk, Byun, Hyeongmin, Ha, Sungjoo
We tackle the problem of automatic portrait matting on mobile devices. The proposed model is aimed at attaining real-time inference on mobile devices with minimal degradation of model performance. Our model MMNet, based on multi-branch dilated convol
Externí odkaz:
http://arxiv.org/abs/1904.03816
Autor:
Choi, Seungwoo, Seo, Seokjun, Shin, Beomjun, Byun, Hyeongmin, Kersner, Martin, Kim, Beomsu, Kim, Dongyoung, Ha, Sungjoo
Keyword spotting (KWS) plays a critical role in enabling speech-based user interactions on smart devices. Recent developments in the field of deep learning have led to wide adoption of convolutional neural networks (CNNs) in KWS systems due to their
Externí odkaz:
http://arxiv.org/abs/1904.03814
Publikováno v:
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Improving the performance of on-device audio classification models remains a challenge given the computational limits of the mobile environment. Many studies leverage knowledge distillation to boost predictive performance by transferring the knowledg
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::54ae43765f0787588e0f61beae6fb45c
https://doi.org/10.1109/icassp43922.2022.9747908
https://doi.org/10.1109/icassp43922.2022.9747908
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