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Unsupervised Hyperbolic Metric Learning

CVPR 2021 · Label-efficient and robust learning › Recognition and long-tail · approach: Correspondence and causal representation learning · 49 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use Unsupervised Hyperbolic Metric Learning with Hierarchical Similarity instead of TAC-CCLGoogLeNet unsupervised metric learning · CARS196Recall@1: 46.1 -> 47.7Helped
use Unsupervised Hyperbolic Metric Learning with Hierarchical Similarity instead of TAC-CCLGoogLeNet unsupervised metric learning · CUB-200-2011Recall@1: 57.5 -> 58.9Helped
use Unsupervised Hyperbolic Metric Learning with Hierarchical Similarity instead of TAC-CCLGoogLeNet unsupervised metric learning · SOPRecall@1: 63.9 -> 65.1Helped
add a hyperbolic layer (ablation)Contrastive Loss unsupervised metric learning · CARS196Recall@1: 33.3 -> 43.7Helped
add a hyperbolic layer (ablation)Contrastive Loss unsupervised metric learning · CUB-200-2011Recall@1: 48.8 -> 54.5Helped
add a hyperbolic layer (ablation)Triplet Loss unsupervised metric learning · CARS196Recall@1: 37.9 -> 44.7Helped
add a hyperbolic layer (ablation)Triplet Loss unsupervised metric learning · CUB-200-2011Recall@1: 50.7 -> 55.1Helped
add a hyperbolic layer (ablation)LiftedStructure Loss unsupervised metric learning · CARS196Recall@1: 42.4 -> 44.3Helped
add a hyperbolic layer (ablation)LiftedStructure Loss unsupervised metric learning · CUB-200-2011Recall@1: 53.1 -> 55.1Helped
add hierarchical clustering and the log-ratio loss with hierarchical similarity beyond the hyperbolic layer (ablation)GoogLeNet unsupervised metric learning · CARS196Recall@1: 44.7 -> 47.7Helped
add hierarchical clustering and the log-ratio loss with hierarchical similarity beyond the hyperbolic layer (ablation)GoogLeNet unsupervised metric learning · CUB-200-2011Recall@1: 55.1 -> 58.9Helped
increase the number of hierarchical similarity levels (ablation)Unsupervised Hyperbolic Metric Learning with Hierarchical Similarity · CUB-200-2011Recall@1Mixed

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