no code implementations • 24 Aug 2022 • Mocho Go, Hideyuki Tachibana
Following the success in language domain, the self-attention mechanism (transformer) is adopted in the vision domain and achieving great success recently.
Ranked #32 on
Instance Segmentation
on COCO test-dev
no code implementations • 26 Dec 2021 • Hideyuki Tachibana, Mocho Go, Muneyoshi Inahara, Yotaro Katayama, Yotaro Watanabe
Diffusion generative models have emerged as a new challenger to popular deep neural generative models such as GANs, but have the drawback that they often require a huge number of neural function evaluations (NFEs) during synthesis unless some sophisticated sampling strategies are employed.
no code implementations • 22 Oct 2020 • Hideyuki Tachibana
In neural network-based monaural speech separation techniques, it has been recently common to evaluate the loss using the permutation invariant training (PIT) loss.
1 code implementation • 21 Sep 2020 • Hideyuki Tachibana, Yotaro Katayama
The authors applied this technique to an existing large vocabulary Japanese dictionary NEologd, and obtained a large vocabulary Japanese accent dictionary.
21 code implementations • 24 Oct 2017 • Hideyuki Tachibana, Katsuya Uenoyama, Shunsuke Aihara
This paper describes a novel text-to-speech (TTS) technique based on deep convolutional neural networks (CNN), without use of any recurrent units.