FedH2L: Federated Learning with Model and Statistical Heterogeneity

no code implementations • 27 Jan 2021 • Yiying Li, Wei Zhou, Huaimin Wang, Haibo Mi, Timothy M. Hospedales

Federated learning (FL) enables distributed participants to collectively learn a strong global model without sacrificing their individual data privacy.

Federated Learning

General audio tagging with ensembling convolutional neural network and statistical features

2 code implementations • 30 Oct 2018 • Kele Xu, Boqing Zhu, Qiuqiang Kong, Haibo Mi, Bo Ding, Dezhi Wang, Huaimin Wang

Audio tagging is challenging due to the limited size of data and noisy labels.

Audio Tagging Descriptive +2

Collaborative Deep Learning Across Multiple Data Centers

no code implementations • 16 Oct 2018 • Kele Xu, Haibo Mi, Dawei Feng, Huaimin Wang, Chuan Chen, Zibin Zheng, Xu Lan

Valuable training data is often owned by independent organizations and located in multiple data centers.

Sample Dropout for Audio Scene Classification Using Multi-Scale Dense Connected Convolutional Neural Network

no code implementations • 12 Jun 2018 • Dawei Feng, Kele Xu, Haibo Mi, Feifan Liao, Yan Zhou

In this paper, we explore the use of multi-scale Dense connected convolutional neural network (DenseNet) for the classification task, with the goal to improve the classification performance as multi-scale features can be extracted from the time-frequency representation of the audio signal.

Acoustic Scene Classification Classification +3

Mixup-Based Acoustic Scene Classification Using Multi-Channel Convolutional Neural Network

no code implementations • 18 May 2018 • Kele Xu, Dawei Feng, Haibo Mi, Boqing Zhu, Dezhi Wang, Lilun Zhang, Hengxing Cai, Shuwen Liu

Audio scene classification, the problem of predicting class labels of audio scenes, has drawn lots of attention during the last several years.

Acoustic Scene Classification Classification +2