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Hypernym Detection Using Strict Partial Order Networks

AAAI 2020 · Detection and segmentation › Open-set and few-shot detection · approach: Object-level representation learning · 9 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use SPON rather than HyperbolicConesUnsupervised hypernym detection · LEDSAverage Precision: .89 -> .91Helped
use SPON rather than HyperbolicConesUnsupervised direction prediction · BLESSAverage Accuracy: .94 -> .97Helped
use SPON rather than HyperbolicConesUnsupervised direction prediction · WBLESSAverage Accuracy: .90 -> .91Helped
use SPON rather than HyperbolicConesUnsupervised hypernym detection · BLESSAverage Precision: .81 -> .81No clear effect
use SPON rather than HyperbolicConesUnsupervised direction prediction · BIBLESSAverage Accuracy: .87 -> .87No clear effect
use SPON rather than CRIMSemEval hypernym discovery · MusicMAP: 40.97 -> 54.70Helped
use SPON rather than CRIMSemEval hypernym discovery · MedicalMAP: 34.05 -> 33.50Hurt
add the Residual connection (ablation)SPON · BLESSAverage Precision: .73 -> .81Helped
add the Residual connection (ablation)SPON · EVALAverage Precision: .49 -> .50Helped
add the Residual connection (ablation)SPON · LEDSAverage Precision: .82 -> .91Helped
add the Residual connection (ablation)SPON · WBLESSAverage Precision: .96 -> .98Helped
replace RELU with Tanh while retaining Residual (ablation)SPON · BLESSAverage Precision: .81 -> .79Hurt

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