Consequently, relying solely on communication noise, as done in the multiple-input single-output system, cannot meet high privacy requirements, and a device-side privacy-preserving mechanism is necessary for optimal DP design.
Sensor drift is a long-existing unpredictable problem that deteriorates the performance of gaseous substance recognition, calling for an antidrift domain adaptation algorithm.
In order to do effective optimization in the second stage, counterfactual prediction and noise-reduction are essential for the first stage.
We propose a deep bilinear model for blind image quality assessment (BIQA) that handles both synthetic and authentic distortions.
Ranked #2 on Video Quality Assessment on MSU NR VQA Database
No-reference image quality assessment (NR-IQA) aims to measure the image quality without reference image.
Also, we analyzed the factors that could influence the performance from two aspects: the architecture of the deep neural network and the contribution of local and scene-aware information.