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Consensus Style Centralizing Auto-Encoder for Weak Style Classification

AAAI 2016 · Label-efficient and robust learning › Recognition and long-tail · approach: Correspondence and causal representation learning · 24 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add style centralizing auto-encoder (SCAE) on top of concatenated style descriptor (ablation)style descriptor · Hipster Wars p=0.1accuracy (%): 77.73 -> 84.37Helped
add multi-column late fusion (MC-SCAE) to SCAE (ablation)SCAE · Hipster Wars p=0.1accuracy (%): 84.37 -> 87.42Helped
add column consensus constraint to MC-SCAE (CSCAE) (ablation)MC-SCAE · Hipster Wars p=0.1accuracy (%): 87.42 -> 89.21Helped
add column consensus constraint to MC-SCAE (ablation)MC-SCAE · Hipster Wars p=0.2accuracy (%): 77.00 -> 75.32Hurt
add column consensus constraint to MC-SCAE (ablation)MC-SCAE · Online Shoppingaccuracy (%): 66.15 -> 70.41 (phi=7); 53.28 -> 51.18 (phi=4)Mixed
add multi-column fusion to SCAE (ablation)SCAE · Online Shopping phi=7accuracy (%): 74.33 -> 66.15Hurt
add column consensus constraint to MC-SCAE (ablation)MC-SCAE · Mangaaccuracy (%): 85.38 -> 86.23 (p=0.1); 69.58 -> 68.63 (p=0.4)Mixed
use CSCAE instead of [MM, 2014] featuresSVM on manga features · Manga p=0.5accuracy (%): 60.07 -> 67.35Helped
use CSCAE instead of [ECCV, 2014] style descriptornearest neighbor on style descriptor · Online Shopping phi=4accuracy (%): 48.63 -> 51.18Helped

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