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Improving model calibration with accuracy versus uncertainty optimization
RanganathKrishnan, OmeshTickoo....
Published date-12/01/2020
ImageClassification, VariationalInference
Obtaining reliable and accurate quantification of uncertainty estimates from deep neural networks is important in safety-critical applications. A well-calibrated model should be accurate when it is certain about its prediction …
ICNet: Intra-saliency Correlation Network for Co-Saliency Detection
Wen-DaJin, JunXu, Ming-MingCheng....
Published date-12/01/2020
SaliencyDetection
Intra-saliency and inter-saliency cues have been extensively studied for co-saliency detection (Co-SOD). Model-based methods produce coarse Co-SOD results due to hand-crafted intra- and inter-saliency features. Current data-driven models exploit inter-saliency …
Human Parsing Based Texture Transfer from Single Image to 3D Human via Cross-View Consistency
FangZhao, ShengcaiLiao, KaihaoZhang....
Published date-12/01/2020
HumanParsing, SemanticParsing, TextureSynthesis
This paper proposes a human parsing based texture transfer model via cross-view consistency learning to generate the texture of 3D human body from a single image. We use the semantic …
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, …
Diversity-Guided Multi-Objective Bayesian Optimization With Batch Evaluations
MinaKonakovicLukovic, YunshengTian, WojciechMatusik....
Published date-12/01/2020
Many science, engineering, and design optimization problems require balancing the trade-offs between several conflicting objectives. The objectives are often black-box functions whose evaluations are time-consuming and costly. Multi-objective Bayesian optimization …
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 …