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Make an Omelette with Breaking Eggs: Zero-Shot Learning for Novel Attribute Synthesis

NeurIPS 2022 · Label-efficient and robust learning › Zero-/few-shot and meta-learning · approach: Generative feature synthesis for zero-shot · 1 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use ZSLA instead of the strongest baselinenovel attribute detection on CUB with 32 seen attributes · CUBmAUROC: 0.626 -> 0.689Helped
Use ZSLA instead of the strongest baselinenovel attribute detection on CUB with 32 seen attributes · CUBmAP@50: 0.223 -> 0.320Helped
Use ZSLA instead of the strongest baselinenovel attribute detection on CUB with 32 seen attributes · CUBmLA: 0.782 -> 0.846Helped
Re-annotate with ZSLA-synthesized attributes instead of using 32 manual attributesCADAVAE GZSL training on CUB · CUB GZSLH: 33.4 -> 53.2Helped
Re-annotate with ZSLA-synthesized attributes instead of using 32 manual attributesTFVAEGAN GZSL training on CUB · CUB GZSLH: 37.1 -> 57.4Helped
Re-annotate with ZSLA-synthesized attributes instead of using 32 manual attributesALE GZSL training on CUB · CUB GZSLH: 13.7 -> 36.1Helped
Re-annotate with ZSLA-synthesized attributes instead of using 32 manual attributesESZSL GZSL training on CUB · CUB GZSLH: 15.9 -> 26.2Helped
Add uni-modal constraint (ablation)ZSLA novel attribute detectors without location information · CUBmLA: .348 -> .613Helped
Add ground-truth location information (ablation)ZSLA novel attribute detectors with UMC · CUBmLA: .613 -> .846Helped
Add location information and UMC (ablation)ZSLA novel attribute detectors · CUBmAUROC: .702 -> .689No clear effect
Add location information and UMC (ablation)ZSLA novel attribute detectors · CUBmAP@50: .325 -> .320No clear effect

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