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Found 6 papers, 3 papers with code
LMD: Light-weight Prediction Quality Estimation for Object Detection in Lidar Point Clouds
In order to address these two issues, we propose LidarMetaDetect (LMD), a light-weight post-processing scheme for prediction quality estimation.
False Positive Detection and Prediction Quality Estimation for LiDAR Point Cloud Segmentation
We present a novel post-processing tool for semantic segmentation of LiDAR point cloud data, called LidarMetaSeg, which estimates the prediction quality segmentwise.
MetaBox+: A new Region Based Active Learning Method for Semantic Segmentation using Priority Maps
The comparison and analysis of the results provide insights into annotation costs as well as robustness and variance of the methods.
On the Robustness of Active Learning
Active Learning is concerned with the question of how to identify the most useful samples for a Machine Learning algorithm to be trained with.
Fast and Reliable Architecture Selection for Convolutional Neural Networks
The performance of a Convolutional Neural Network (CNN) depends on its hyperparameters, like the number of layers, kernel sizes, or the learning rate for example.
EffNet: An Efficient Structure for Convolutional Neural Networks
With the ever increasing application of Convolutional Neural Networks to customer products the need emerges for models to efficiently run on embedded, mobile hardware.
