ESPNet is a convolutional neural network for semantic segmentation of high resolution images under resource constraints. ESPNet is based on a convolutional module, efficient spatial pyramid (ESP), which is efficient in terms of computation, memory, and power.

Source: ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation

Latest Papers

PAPER DATE
A Crowdsourced Open-Source Kazakh Speech Corpus and Initial Speech Recognition Baseline
Yerbolat KhassanovSaida MussakhojayevaAlmas MirzakhmetovAlen AdiyevMukhamet NurpeiissovHuseyin Atakan Varol
2020-09-22
Frame-To-Frame Consistent Semantic Segmentation
Manuel RebolPatrick Knöbelreiter
2020-08-03
ESPnet-TTS: Unified, Reproducible, and Integratable Open Source End-to-End Text-to-Speech Toolkit
| Tomoki HayashiRyuichi YamamotoKatsuki InoueTakenori YoshimuraShinji WatanabeTomoki TodaKazuya TakedaYu ZhangXu Tan
2019-10-24
Semi-supervised Sequence-to-sequence ASR using Unpaired Speech and Text
Murali Karthick BaskarShinji WatanabeRamon AstudilloTakaaki HoriLukáš BurgetJan Černocký
2019-04-30
C3: Concentrated-Comprehensive Convolution and its application to semantic segmentation
| Hyojin ParkYoungjoon YooGeonseok SeoDongyoon HanSangdoo YunNojun Kwak
2018-12-12
ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network
| Sachin MehtaMohammad RastegariLinda ShapiroHannaneh Hajishirzi
2018-11-28
ESPnet: End-to-End Speech Processing Toolkit
Shinji WatanabeTakaaki HoriShigeki KaritaTomoki HayashiJiro NishitobaYuya UnnoNelson Enrique Yalta SoplinJahn HeymannMatthew WiesnerNanxin ChenAdithya RenduchintalaTsubasa Ochiai
2018-03-30
ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation
| Sachin MehtaMohammad RastegariAnat CaspiLinda ShapiroHannaneh Hajishirzi
2018-03-19

Categories