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OSAN: A One-Stage Alignment Network To Unify Multimodal Alignment and Unsupervised Domain Adaptation

CVPR 2023 · Label-efficient and robust learning › Domain adaptation and generalization · approach: Feature alignment for unsupervised adaptation · 15 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use one-stage TAL and DDG alignment rather than the two-stage MDMN approachMultimodal UDA · Text-news → Text-showF1: 88.34 → 90.53Helped
Use DDG instead of MixupOSAN with TAL · Text-news → Text-showF1: 89.35 → 90.53Helped
Add DDG to OSAN (ablation)OSAN · CMU-MOSIMAE: +0.039Helped
Add TAL to OSAN (ablation)OSAN · CMU-MOSIMAE: +0.051Helped
Add both TAL and DDG (ablation)OSAN · CMU-MOSIMAE: +0.074Helped
Add adversarial and discrepancy losses to classification loss (ablation)OSAN · CMU-MOSIMAE: +0.018Helped
Add discrepancy loss alongside classification and adversarial losses (ablation)OSAN · CMU-MOSIMAE: +0.009Helped
Add adversarial loss alongside classification and discrepancy losses (ablation)OSAN · CMU-MOSIMAE: +0.016Helped
Set DDG fusion coefficient to 0.85 rather than 0.9 (ablation)OSAN · CMU-MOSIMAE: +0.004Helped
Use DDG multi-head count 64 rather than 32 (ablation)OSAN · CMU-MOSIMAE: 0.001Helped
Add DDG to OSAN (ablation)OSAN · Text-showF1: -1.09Helped
Add TAL to OSAN (ablation)OSAN · Text-showF1: -1.55Helped

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