Search Results for author:
Found 9 papers, 1 papers with code
PECI-Net: Bolus segmentation from video fluoroscopic swallowing study images using preprocessing ensemble and cascaded inference
Bolus segmentation is crucial for the automated detection of swallowing disorders in videofluoroscopic swallowing studies (VFSS).
Unidirectional Thin Adapter for Efficient Adaptation of Deep Neural Networks
In this paper, we propose a new adapter network for adapting a pre-trained deep neural network to a target domain with minimal computation.
MuSE-SVS: Multi-Singer Emotional Singing Voice Synthesizer that Controls Emotional Intensity
We propose a multi-singer emotional singing voice synthesizer, Muse-SVS, that expresses emotion at various intensity levels by controlling subtle changes in pitch, energy, and phoneme duration while accurately following the score.
One-shot Ultra-high-Resolution Generative Adversarial Network That Synthesizes 16K Images On A Single GPU
We propose a one-shot ultra-high-resolution generative adversarial network (OUR-GAN) framework that generates non-repetitive 16K (16, 384 x 8, 640) images from a single training image and is trainable on a single consumer GPU.
UniTTS: Residual Learning of Unified Embedding Space for Speech Style Control
We propose a novel high-fidelity expressive speech synthesis model, UniTTS, that learns and controls overlapping style attributes avoiding interference.
Fast DCTTS: Efficient Deep Convolutional Text-to-Speech
We propose an end-to-end speech synthesizer, Fast DCTTS, that synthesizes speech in real time on a single CPU thread.
VocGAN: A High-Fidelity Real-time Vocoder with a Hierarchically-nested Adversarial Network
Additionally, compared with Parallel WaveGAN, another recently developed high-fidelity vocoder, VocGAN is 6. 98x faster on a CPU and exhibits higher MOS.
Capsule Networks Need an Improved Routing Algorithm
The experimental results show that the routing algorithms do not behave as expected and often produce results that are worse than simple baseline algorithms that assign the connection strengths uniformly or randomly.
Overcoming Catastrophic Forgetting by Neuron-level Plasticity Control
To address the issue of catastrophic forgetting in neural networks, we propose a novel, simple, and effective solution called neuron-level plasticity control (NPC).
