The article explores the effectiveness of hyperbolic geometry in enhancing image classification methods. It highlights the performance of probabilistic trees in defining hierarchical structures, showing significant improvement over deterministic methods. The study's findings affirm that hyperbolic models provide a more stable framework for these tasks, particularly emphasizing the utility of hierarchical contrastive loss and KL loss terms in improving accuracy for classifying images.
The results demonstrate that applying hierarchical contrastive loss in Euclidean space degrades performance, indicating hyperbolic space is more suitable for stabilizing hierarchical structures.
For constructing a hierarchy tree, probabilistic modeling defines every node via MoG of its child node distributions, while the deterministic approach determines each node the mean of its child nodes.
The probabilistic hierarchy tree achieves significant improvement in performance compared to the deterministic hierarchy tree, confirming the advantages of probabilistic modeling in hierarchical classifications.
#hyperbolic-geometry #image-classification #hierarchical-structures #probabilistic-modeling #machine-learning
Collection
[
|
...
]