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KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation
Hao-ZheFeng, ZhaoyangYou, MinghaoChen....
Published date-11/19/2020
DomainAdaptation, UnsupervisedDomainAdaptation
Conventional unsupervised multi-source domain adaptation (UMDA) methods assume all source domains can be accessed directly. This neglects the privacy-preserving policy, that is, all the data and computations must be kept …
Dense Label Encoding for Boundary Discontinuity Free Rotation Detection
XueYang, LipingHou, YueZhou....
Published date-11/19/2020
SceneText
Rotation detection serves as a fundamental building block in many visual applications involving aerial image, scene text, and face etc. Differing from the dominant regression-based approaches for orientation estimation, this …
Robustness to Missing Features using Hierarchical Clustering with Split Neural Networks
RishabKhincha, UtkarshSarawgi, WazeerZulfikar....
Published date-11/19/2020
Clustering, Imputation
The problem of missing data has been persistent for a long time and poses a major obstacle in machine learning and statistical data analysis. Past works in this field have …
FedEval: A Benchmark System with a Comprehensive Evaluation Model for Federated Learning
DiChai, LeyeWang, KaiChen....
Published date-11/19/2020
FederatedLearning
As an innovative solution for privacy-preserving machine learning (ML), federated learning (FL) is attracting much attention from research and industry areas. While new technologies proposed in the past few years …
Improving Bayesian Network Structure Learning in the Presence of Measurement Error
YangLiu, AnthonyC.Constantinou, ZhigaoGuo....
Published date-11/19/2020
Structure learning algorithms that learn the graph of a Bayesian network from observational data often do so by assuming the data correctly reflect the true distribution of the variables. However, …
Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning
ZhendaXie, YutongLin, ZhengZhang....
Published date-11/19/2020
ContrastiveLearning, ObjectDetection, RepresentationLearning, SemanticSegmentation
Contrastive learning methods for unsupervised visual representation learning have reached remarkable levels of transfer performance. We argue that the power of contrastive learning has yet to be fully unleashed, as …