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Found 4 papers, 1 papers with code
Why Discard if You Can Recycle?: A Recycling Max Pooling Module for 3D Point Cloud Analysis
In order to address this issue and improve the performance of any baseline 3D point classification or segmentation model, we propose a new module, referred to as the Recycling MaxPooling (RMP) module, to recycle and utilize the features of some of the discarded points.
Background-Aware 3D Point Cloud Segmentationwith Dynamic Point Feature Aggregation
As the core module of the DPFA-Net, we propose a Feature Aggregation layer, in which features of the dynamic neighborhood of each point are aggregated via a self-attention mechanism.
Autonomously and Simultaneously Refining Deep Neural Network Parameters by a Bi-Generative Adversarial Network Aided Genetic Algorithm
The choice of parameters, and the design of the network architecture are important factors affecting the performance of deep neural networks.
Autonomously and Simultaneously Refining Deep Neural Network Parameters by Generative Adversarial Networks
Our proposed approach can be used to autonomously refine the parameters, and improve the accuracy of different deep neural network architectures.
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