313 citations · 390 across the 14 of their papers we have counts for
21 papers
Revisiting Random Channel Pruning for Neural Network Compression
Yawei Li, Kamil Adamczewski, Wen Li +3
Channel (or 3D filter) pruning serves as an effective way to accelerate the inference of neural networks. There has been a flurry of algorithms that try to solve this practical pro…
Revisiting Deep Semi-supervised Learning: An Empirical Distribution Alignment Framework and Its Generalization Bound
Feiyu Wang, Qin Wang, Wen Li +2
In this work, we revisit the semi-supervised learning (SSL) problem from a new perspective of explicitly reducing empirical distribution mismatch between labeled and unlabeled samp…
Sparse-to-dense Feature Matching: Intra and Inter domain Cross-modal Learning in Domain Adaptation for 3D Semantic Segmentation
Duo Peng, Yinjie Lei, Wen Li +2
Domain adaptation is critical for success when confronting with the lack of annotations in a new domain. As the huge time consumption of labeling process on 3D point cloud, domain…
Real-Time Video Super-Resolution on Smartphones with Deep Learning, Mobile AI 2021 Challenge: Report
Andrey Ignatov, Andres Romero, Heewon Kim +28
Video super-resolution has recently become one of the most important mobile-related problems due to the rise of video communication and streaming services. While many solutions hav…
VDM-DA: Virtual Domain Modeling for Source Data-free Domain Adaptation
Jiayi Tian, Jing Zhang, Wen Li +1
Domain adaptation aims to leverage a label-rich domain (the source domain) to help model learning in a label-scarce domain (the target domain). Most domain adaptation methods requi…
Cluster, Split, Fuse, and Update: Meta-Learning for Open Compound Domain Adaptive Semantic Segmentation
Rui Gong, Yuhua Chen, Danda Pani Paudel +5
Open compound domain adaptation (OCDA) is a domain adaptation setting, where target domain is modeled as a compound of multiple unknown homogeneous domains, which brings the advant…