Search Results for author:
Found 7 papers, 3 papers with code
Cascaded Local Implicit Transformer for Arbitrary-Scale Super-Resolution
Implicit neural representation has recently shown a promising ability in representing images with arbitrary resolutions.
Self-Supervised Robustifying Guidance for Monocular 3D Face Reconstruction
Therefore, we propose a Self-Supervised RObustifying GUidancE (ROGUE) framework to obtain robustness against occlusions and noise in the face images.
Learning to Compensate: A Deep Neural Network Framework for 5G Power Amplifier Compensation
In the proposed framework, Deep Neural Networks (DNNs) are used to learn the characteristics of the PAs, while, correspondent Digital Pre-Distortions (DPDs) are also learned to compensate for the nonlinear and memory effects of PAs.
Network Space Search for Pareto-Efficient Spaces
We propose an NSS method to directly search for efficient-aware network spaces automatically, reducing the manual effort and immense cost in discovering satisfactory ones.
Deploying Image Deblurring across Mobile Devices: A Perspective of Quality and Latency
To the best of our knowledge, this is the first paper that addresses all the deployment issues of image deblurring task across mobile devices.
Unified Dynamic Convolutional Network for Super-Resolution with Variational Degradations
In SISR with non-blind setting, our Unified Dynamic Convolutional Network for Variational Degradations (UDVD) is evaluated on both synthetic and real images with an extensive set of variations.
Architecture-aware Network Pruning for Vision Quality Applications
Convolutional neural network (CNN) delivers impressive achievements in computer vision and machine learning field.
