2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

Technical Program

Paper Detail

Paper IDCI-1.6
Paper Title MULTI-INITIALIZATION META-LEARNING WITH DOMAIN ADAPTATION
Authors Zhengyu Chen, Donglin Wang, Westlake University, China
SessionCI-1: Theory for Computational Imaging
LocationGather.Town
Session Time:Wednesday, 09 June, 15:30 - 16:15
Presentation Time:Wednesday, 09 June, 15:30 - 16:15
Presentation Poster
Topic Computational Imaging: [IMT] Computational Imaging Methods and Models
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Virtual Presentation  Click here to watch in the Virtual Conference
Abstract Recently, meta learning providing multiple initializations has drawn much attention due to its capability of handling multi-modal tasks drawn from diverse distributions. However, because of the difference of class distribution between meta-training and meta-test domain, the domain shift occurs in multi-modal meta-learning setting. To improve the performance on multi-modal tasks, we propose multi-initialization meta-learning with domain adaptation (MIML-DA) to tackle such domain shift. MIML-DA consists of a modulation network and a novel meta separation network (MSN), where the modulation network is to encode tasks into common and private modulation vectors, and then MSN uses these vectors separately to update the cross-domain meta-learner via a double-gradient descent process. In addition, the regularization using inequality measure is considered to improve the generalization ability of the meta-learner. Extensive experiments demonstrate the effectiveness of our MIML-DA method to new multi-modal tasks.