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Research map / Label-efficient and robust learning / Zero-/few-shot and meta-learning

Zero-shot Learning via Simultaneous Generating and Learning

NeurIPS 2019 · Label-efficient and robust learning › Zero-/few-shot and meta-learning · approach: Generative feature synthesis for zero-shot · 29 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add simultaneous generating-and-learning on unseen classes (ablation)mmVAE · AwA2 GZSLharmonic mean H: 26.9 -> 65.3Helped
add simultaneous generating-and-learning on unseen classes (ablation)mmVAE · AwA1 GZSLharmonic mean H: 54.2 -> 61.5Helped
add simultaneous generating-and-learning on unseen classes (ablation)mmVAE · CUB GZSLharmonic mean H: 39.3 -> 47.0Helped
add simultaneous generating-and-learning on unseen classes (ablation)mmVAE · SUN GZSLharmonic mean H: 21.4 -> 34.9Helped
add simultaneous generating-and-learning on unseen classes (ablation)mmVAE · AwA 10-way 0-shotaccuracy: 74.2 -> 84.1Helped
add simultaneous generating-and-learning on unseen classes (ablation)mmVAE · CUB 50-way 0-shotaccuracy: 58.4 -> 62.5Helped
add simultaneous generating-and-learning on unseen classes (ablation)mmVAE · AwA2 GZSLseen accuracy s: 92.6 -> 86.3Hurt
activate dropout when sampling unseen datapoints (ablation)SGAL · AwA2 GZSLharmonic mean H: 65.3 -> 65.6Helped
activate dropout when sampling unseen datapoints (ablation)SGAL · SUN GZSLunseen accuracy u: 35.5 -> 42.9Helped
activate dropout when sampling unseen datapoints (ablation)SGAL · CUB GZSLharmonic mean H: 47.0 -> 45.9Hurt
activate dropout when sampling unseen datapoints (ablation)SGAL · AwA1 GZSLharmonic mean H: 61.5 -> 62.2Helped

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