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Domain-Invariant Projection Learning for Zero-Shot Recognition

NeurIPS 2018 · Label-efficient and robust learning › Zero-/few-shot and meta-learning · approach: Generative feature synthesis for zero-shot · 40 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add transductive learning with unlabelled unseen-class data to the bidirectional DIPL0 model (ablation)DIPL0 · Pure ZSL, four medium-scale datasetsaccuracy (%): 5% to 30%Helped
Add superclass-based alignment (ablation)DIPL1 · Pure ZSL, four medium-scale datasetsaccuracy (%): about 1--2%Helped
Add forward projection alongside reverse projection (ablation)RPL · Pure ZSL, four medium-scale datasetsaccuracy (%): 2--4%Helped
Replace the min-min formulation with a softmax-based soft assignment (ablation)DIPL · Zero-shot recognitionperformanceHurt
Use the full DIPL model rather than the strongest reported baselineZero-shot recognition · AwA, CUB, aPY, SUN, and ImNet, standard ZSLaccuracy or hit@5 accuracy (%): 96.1 vs 95.0; 68.2 vs 67.7; 87.8 vs 69.7; 93.5 vs 91.5; 70.0 vs 69.6; 31.7 vs 27.2Helped
Use the full DIPL model rather than the strongest reported baselineZero-shot recognition · AwA, CUB, aPY, and SUN, pure ZSLaccuracy (%): 85.6 vs 68.2; 65.4 vs 58.1; 69.6 vs 44.8; 67.9 vs 60.0Helped
Use the full DIPL model rather than the strongest reported baselineZero-shot recognition · AwA and CUB, generalized ZSLHM (%): 75.6 vs 52.8; 43.2 vs 39.2Helped

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