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SCGAN: Saliency Map-guided Colorization with Generative Adversarial Network
YuzhiZhao, Lai-ManPo, Kwok-WaiCheung....
Published date-11/23/2020
Colorization
Given a grayscale photograph, the colorization system estimates a visually plausible colorful image. Conventional methods often use semantics to colorize grayscale images. However, in these methods, only classification semantic information …
RobustPointSet: A Dataset for Benchmarking Robustness of Point Cloud Classifiers
SaeidAsgariTaghanaki, JieliangLuo, RanZhang....
Published date-11/23/2020
Classify3DPointClouds, Pointcloudclassificationdataset
The 3D deep learning community has seen significant strides in pointcloud processing over the last few years. However, the datasets on which deep models have been trained have largely remained …
Stacked Graph Filter
HoangNT, TakanoriMaehara, TsuyoshiMurata....
Published date-11/22/2020
We study Graph Convolutional Networks (GCN) from the graph signal processing viewpoint by addressing a difference between learning graph filters with fully connected weights versus trainable polynomial coefficients. We find …
Time series classification for predictive maintenance on event logs
AntoineGuillaume, ChristelVrain, ElloumiWael....
Published date-11/22/2020
TimeSeries, TimeSeriesClassification
Time series classification (TSC) gained a lot of attention in the past decade and number of methods for representing and classifying time series have been proposed. Nowadays, methods based on …
Learning a Deep Generative Model like a Program: the Free Category Prior
EliSennesh....
Published date-11/22/2020
Programinduction
Humans surpass the cognitive abilities of most other animals in our ability to "chunk" concepts into words, and then combine the words to combine the concepts. In this process, we …
Multiresolution Knowledge Distillation for Anomaly Detection
MohammadrezaSalehi, NioushaSadjadi, SorooshBaselizadeh....
Published date-11/22/2020
AnomalyDetection, RepresentationLearning, UnsupervisedRepresentationLearning
Unsupervised representation learning has proved to be a critical component of anomaly detection/localization in images. The challenges to learn such a representation are two-fold. Firstly, the sample size is not …