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Found 5 papers, 1 papers with code
Learning Convolutional Neural Networks in the Frequency Domain
The most important motivation of this research is that we can use the straightforward element-wise multiplication operation to replace the image convolution in the frequency domain based on the Cross-Correlation Theorem, which obviously reduces the computation complexity.
UENAS: A Unified Evolution-based NAS Framework
To alleviate the huge search cost caused by the expanded search space, three strategies are adopted: First, an adaptive pruning strategy that iteratively trims the average model size in the population without compromising performance.
Fixed-size Objects Encoding for Visual Relationship Detection
Instead, we propose a novel method to encode all background objects in each image by using one fixed-size vector (i. e., FBE vector).
STMARL: A Spatio-Temporal Multi-Agent Reinforcement Learning Approach for Cooperative Traffic Light Control
Moreover, based on the temporally-dependent traffic information, we design a Graph Neural Network based model to represent relationships among multiple traffic lights, and the decision for each traffic light will be made in a distributed way by the deep Q-learning method.
DropFilter: A Novel Regularization Method for Learning Convolutional Neural Networks
Most of previous methods mainly consider to drop features from input data and hidden layers, such as Dropout, Cutout and DropBlocks.
