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Found 6 papers, 2 papers with code
Combining pretrained CNN feature extractors to enhance clustering of complex natural images
First, extensive experiments are conducted and show that, for a given dataset, the choice of the CNN architecture for feature extraction has a huge impact on the final clustering.
Semantically Meaningful View Selection
Although deep learning has facilitated progress in image understanding, a robot's performance in problems like object recognition often depends on the angle from which the object is observed.
Automatic Construction of Real-World Datasets for 3D Object Localization using Two Cameras
Unlike classification, position labels cannot be assigned manually by humans.
CNN features are also great at unsupervised classification
This paper aims at providing insight on the transferability of deep CNN features to unsupervised problems.
Clustering for Different Scales of Measurement - the Gap-Ratio Weighted K-means Algorithm
This paper describes a method for clustering data that are spread out over large regions and which dimensions are on different scales of measurement.
Learning local trajectories for high precision robotic tasks : application to KUKA LBR iiwa Cartesian positioning
By exploration and regression, linear and quadratic models are learnt for respectively the dynamics and cost function.
