Search Results for author:
Found 8 papers, 1 papers with code
Fed2: Feature-Aligned Federated Learning
Federated learning learns from scattered data by fusing collaborative models from local nodes.
Heterogeneous Federated Learning
Specifically, we design a feature-oriented regulation method ({$\Psi$-Net}) to ensure explicit feature information allocation in different neural network structures.
Interpreting and Evaluating Neural Network Robustness
Recently, adversarial deception becomes one of the most considerable threats to deep neural networks.
INTERPRETABLE CONVOLUTIONAL FILTER PRUNING
As significant redundancies inevitably present in such a structure, many works have been proposed to prune the convolutional filters for computation cost reduction.
Demystifying Neural Network Filter Pruning
We find that the filter magnitude based method fails to eliminate the filters with repetitive functionality.
Distilling Critical Paths in Convolutional Neural Networks
Neural network compression and acceleration are widely demanded currently due to the resource constraints on most deployment targets.
Functionality-Oriented Convolutional Filter Pruning
As significant redundancies inevitably present in such a structure, many works have been proposed to prune the convolutional filters for computation cost reduction.
How convolutional neural network see the world - A survey of convolutional neural network visualization methods
Nowadays, the Convolutional Neural Networks (CNNs) have achieved impressive performance on many computer vision related tasks, such as object detection, image recognition, image retrieval, etc.
