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Subsidiary Prototype Alignment for Universal Domain Adaptation

NeurIPS 2022 · Label-efficient and robust learning › Domain adaptation and generalization · approach: Feature alignment for unsupervised adaptation · 10 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add SPA (word-prototypes + pretext task + entropy loss)OVANet · UniDA Office-HomeHOS: 71.8 -> 73.2Helped
add SPADCC · UniDA Office-HomeHOS: 70.2 -> 74.9Helped
add SPAOVANet · UniDA DomainNetHOS: 50.7 -> 52.2Helped
add SPADCC · OSDA Office-31HOS: 86.8 -> 93.2Helped
add architecture modification only (ablation)OVANet · UniDA Office-HomeHOS: 71.8 -> 71.8No clear effect
add architecture modification only (ablation)DCC · UniDA Office-HomeHOS: 70.2 -> 70.9Helped
add word-histogram self-entropy loss to arch mod (ablation)OVANet · UniDA Office-HomeHOS: 71.8 -> 72.2Helped
add pretext task without entropy loss (ablation)DCC · UniDA Office-HomeHOS: 70.2 -> 73.6Helped
add entropy loss on top of pretext task (full SPA) (ablation)DCC · UniDA Office-HomeHOS: 73.6 -> 74.9Helped
add entropy loss on top of pretext task (full SPA) (ablation)OVANet · UniDA Office-HomeHOS: 72.8 -> 73.2Helped
use rotation pretext task instead of proposed grid-shuffle instance-count task (ablation)OVANet+arch-mod · UniDA Office-HomeHOS: 73.2 -> 72.4Hurt
use colorization pretext task instead of proposed one (ablation)OVANet+arch-mod · UniDA Office-HomeHOS: 73.2 -> 71.8Hurt

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