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Learning to Adapt to Evolving Domains
HongLiu, MingshengLong, JianminWang....
Published date-12/01/2020
DomainAdaptation, Meta-Learning, TransferLearning, UnsupervisedDomainAdaptation
Domain adaptation aims at knowledge transfer from a labeled source domain to an unlabeled target domain. Current domain adaptation methods have made substantial advances in adapting discrete domains. However, this …
Stochastic Deep Gaussian Processes over Graphs
NaiqiLi, WenjieLi, JifengSun....
Published date-12/01/2020
GaussianProcesses, VariationalInference
In this paper we propose Stochastic Deep Gaussian Processes over Graphs (DGPG), which are deep structure models that learn the mappings between input and output signals in graph domains. The …
Counting People by Estimating People Flows
WeizheLiu, MathieuSalzmann, PascalFua....
Published date-12/01/2020
ActiveLearning, CrowdCounting, OpticalFlowEstimation
Modern methods for counting people in crowded scenes rely on deep networks to estimate people densities in individual images. As such, only very few take advantage of temporal consistency in …
The Dilemma of TriHard Loss and an Element-Weighted TriHard Loss for Person Re-Identification
YihaoLv, YouzhiGu, LiuXinggao....
Published date-12/01/2020
PersonRe-Identification
Triplet loss with batch hard mining (TriHard loss) is an important variation of triplet loss inspired by the idea that hard triplets improve the performance of metric leaning networks. However, …
SRG-Net: Unsupervised Segmentation for Terracotta Warrior Point Cloud with 3D Pointwise CNN methods
YaoHu, GuohuaGeng, KangLi....
Published date-12/01/2020
Clustering
In this paper, we present a seed-region-growing CNN(SRG-Net) for unsupervised part segmentation with 3D point clouds of terracotta warriors. Previous neural network researches in 3D are mainly about supervised classification, …
Almost Surely Stable Deep Dynamics
NathanLawrence, PhilipLoewen, MichaelForbes....
Published date-12/01/2020
We introduce a method for learning provably stable deep neural network based dynamic models from observed data. Specifically, we consider discrete-time stochastic dynamic models, as they are of particular interest …