13 citations · 33 across the 9 of their papers we have counts for
13 papers
Federated Self-Supervised Learning for Acoustic Event Classification
Meng Feng, Chieh-Chi Kao, Qingming Tang +4
Standard acoustic event classification (AEC) solutions require large-scale collection of data from client devices for model optimization. Federated learning (FL) is a compelling fr…
Multi-Task Self-Supervised Pre-Training for Music Classification
Ho-Hsiang Wu, Chieh-Chi Kao, Qingming Tang +4
Deep learning is very data hungry, and supervised learning especially requires massive labeled data to work well. Machine listening research often suffers from limited labeled data…
Unsupervised Pre-training of Bidirectional Speech Encoders via Masked Reconstruction
Weiran Wang, Qingming Tang, Karen Livescu
We propose an approach for pre-training speech representations via a masked reconstruction loss. Our pre-trained encoder networks are bidirectional and can therefore be used direct…
Towards Disentangled Representations for Human Retargeting by Multi-view Learning
Chao Yang, Xiaofeng Liu, Qingming Tang +1
We study the problem of learning disentangled representations for data across multiple domains and its applications in human retargeting. Our goal is to map an input image to an id…
Dependency-aware Attention Control for Unconstrained Face Recognition with Image Sets
Xiaofeng Liu, B. V. K Vijaya Kumar, Chao Yang +2
This paper targets the problem of image set-based face verification and identification. Unlike traditional single media (an image or video) setting, we encounter a set of heterogen…
Variational Sequential Labelers for Semi-Supervised Learning
Mingda Chen, Qingming Tang, Karen Livescu +1
We introduce a family of multitask variational methods for semi-supervised sequence labeling. Our model family consists of a latent-variable generative model and a discriminative l…