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A Three-Stage Self-Training Framework for Semi-Supervised Semantic Segmentation
RihuanKe, AngelicaAviles-Rivero, SaurabhPandey....
Published date-12/01/2020
SemanticSegmentation, Semi-SupervisedSemanticSegmentation
Semantic segmentation has been widely investigated in the community, in which the state of the art techniques are based on supervised models. Those models have reported unprecedented performance at the …
Learning Disentangled Representations and Group Structure of Dynamical Environments
RobinQuessard, ThomasBarrett, WilliamClements....
Published date-12/01/2020
Learning disentangled representations is a key step towards effectively discovering and modelling the underlying structure of environments. In the natural sciences, physics has found great success by describing the universe …
Lipschitz-Certifiable Training with a Tight Outer Bound
SungyoonLee, JaewookLee, SaeromPark....
Published date-12/01/2020
Verifiable training is a promising research direction for training a robust network. However, most verifiable training methods are slow or lack scalability. In this study, we propose a fast and …
A Closer Look at the Training Strategy for Modern Meta-Learning
JiaxinChen, Xiao-MingWu, YankeLi....
Published date-12/01/2020
Few-ShotLearning, Few-shotRegression, Meta-Learning
The support/query (S/Q) episodic training strategy has been widely used in modern meta-learning algorithms and is believed to improve their generalization ability to test environments. This paper conducts a theoretical …
Trading Personalization for Accuracy: Data Debugging in Collaborative Filtering
LongChen, YuanYAO, FengXu....
Published date-12/01/2020
RecommendationSystems
Collaborative filtering has been widely used in recommender systems. Existing work has primarily focused on improving the prediction accuracy mainly via either building refined models or incorporating additional side information, …
Digraph Inception Convolutional Networks
ZekunTong, YuxuanLiang, ChangshengSun....
Published date-12/01/2020
Graph Convolutional Networks (GCNs) have shown promising results in modeling graph-structured data. However, they have difficulty with processing digraphs because of two reasons: 1) transforming directed to undirected graph to …