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Price of Stability in Quality-Aware Federated Learning
The presence of label noise can severely degrade the FL performance, and some existing studies have focused on algorithm design for label denoising.
Interpretable Outlier Summarization
Moreover, to effectively handle high dimensional, highly complex data sets which are hard to summarize with simple rules, we propose a localized STAIR approach, called L-STAIR.
Unknown-Aware Deep Neural Network
Unfortunately, although the strong generalization ability of existing CNNs ensures their accuracy when classifying known objects, it also causes them to often assign an unknown to a target class with high confidence.
Context-Aware Object Detection With Convolutional Neural Networks
Although the state-of-the-art object detection methods are successful in detecting and classifying objects by leveraging deep convolutional neural networks (CNNs), these methods overlook the semantic context which implies the probabilities that different classes of objects occur jointly.
Outlier Detection from Image Data
Modern applications from Autonomous Vehicles to Video Surveillance generate massive amounts of image data.
