Name
Title | ||
|---|---|---|
Greedy Compositional Clustering for Unsupervised Learning of Hierarchical Compositional Models. |
Abstract | ||
|---|---|---|
This paper proposes to integrate a feature pursuit learning process into a greedy bottom-up learning scheme. The algorithm combines the benefits of bottom-up and top-down approaches for learning hierarchical models: It allows to induce the hierarchical structure of objects in an unsupervised manner, while avoiding a hard decision on the activation of parts. We follow the principle of compositionality by assembling higher-order parts from elements of lower layers in the hierarchy. The parts are learned greedily with an EM-type process that iterates between image encoding and part re-learning. The process stops when a candidate part is not able to find a free niche in the image. The algorithm proceeds layer by layer in a bottom-up manner until no further compositions are found. A subsequent top-down process composes the learned hierarchical shape vocabulary into a holistic object model. Experimental evaluation of the approach shows state-of-the-art performance on a domain adaptation task. Moreover, we demonstrate the capability of learning complex, semantically meaningful hierarchical compositional models without supervision. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Computer Vision and Pattern Recognition | Computer science,Unsupervised learning,Artificial intelligence,Cluster analysis,Machine learning |
DocType | Volume | Citations |
Journal | abs/1701.06171 | 0 |
PageRank | References | Authors |
0.34 | 24 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adam Kortylewski | 1 | 28 | 9.57 |
| Clemens Blumer | 2 | 26 | 3.19 |
| Thomas Vetter | 3 | 11 | 2.29 |