activity
20192022
most citedVocGAN: A High-Fidelity Real-time Vocoder with a Hierarchically-nested Adversarial Network

5 citations · 7 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CV2022

Unidirectional Thin Adapter for Efficient Adaptation of Deep Neural Networks

Han Gyel Sun, Hyunjae Ahn, HyunGyu Lee +1

In this paper, we propose a new adapter network for adapting a pre-trained deep neural network to a target domain with minimal computation. The proposed model, unidirectional thin…

eess.AS20212 cited

Fast DCTTS: Efficient Deep Convolutional Text-to-Speech

Minsu Kang, Jihyun Lee, Simin Kim +1

We propose an end-to-end speech synthesizer, Fast DCTTS, that synthesizes speech in real time on a single CPU thread. The proposed model is composed of a carefully-tuned lightweigh…

eess.AS20205 cited

VocGAN: A High-Fidelity Real-time Vocoder with a Hierarchically-nested Adversarial Network

Jinhyeok Yang, Junmo Lee, Youngik Kim +2

We present a novel high-fidelity real-time neural vocoder called VocGAN. A recently developed GAN-based vocoder, MelGAN, produces speech waveforms in real-time. However, it often p…

cs.CV2019

Capsule Networks Need an Improved Routing Algorithm

Inyoung Paik, Taeyeong Kwak, Injung Kim

In capsule networks, the routing algorithm connects capsules in consecutive layers, enabling the upper-level capsules to learn higher-level concepts by combining the concepts of th…

cs.CV2019

Overcoming Catastrophic Forgetting by Neuron-level Plasticity Control

Inyoung Paik, Sangjun Oh, Tae-Yeong Kwak +1

To address the issue of catastrophic forgetting in neural networks, we propose a novel, simple, and effective solution called neuron-level plasticity control (NPC). While learning…