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Dress Fashionably: Learn Fashion Collocation With Deep Mixed-Category Metric Learning

AAAI 2018 · Label-efficient and robust learning › Recognition and long-tail · approach: Correspondence and causal representation learning · 45 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use mixed-category sampling instead of triplet sampling (ablation)metric learning · Fashionbeans item levelRecall@20: 0.20 -> 0.24Helped
use mixed-category sampling instead of Siamese pairs (ablation)metric learning · Fashionbeans item levelRecall@20: 0.16 -> 0.24Helped
use triplet instead of Siamese pairs (ablation)metric learning · Fashionbeans item levelRecall@20: 0.16 -> 0.20Helped
use classification-trained GoogLeNet instead of metric learning (ablation)retrieval embedding · Fashionbeans item levelRecall@20: 0.07 vs 0.24Hurt
increase embedding size from 128 to 384 by concatenating layers (ablation)mixed-category metric learning · Fashionbeans item levelRecall@20: 0.24 -> 0.34Helped
replace hinge loss with HDCLoss (ablation)mixed-category metric learning · Fashionbeans item levelRecall@20: 0.34 -> 0.49Helped
replace hinge loss with AffineLoss (ablation)mixed-category metric learning · Fashionbeans item levelRecall@20: 0.34 -> 0.32Hurt
use PDDM/LiftedStruct/Histogram loss instead of hinge (ablation)mixed-category metric learning · Fashionbeans item levelRecall@20: 0.34 -> 0.44 (PDDM)Helped
use hard-aware sequential exemplar mining instead of random, simple-only or semi-hard-only (ablation)mixed-category metric learning · training lossloss curveHelped

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