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

Generalized Zero-Shot Learning via Disentangled Representation

AAAI 2021 · Label-efficient and robust learning › Zero-/few-shot and meta-learning · approach: Generative feature synthesis for zero-shot · 78 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add feature disentanglement (ablation)CADA-VAE · CUBH: 52.4 -> 53.7Helped
add feature disentanglement (ablation)CADA-VAE · AWA1H: 64.1 -> 65.0Helped
add feature disentanglement (ablation)CADA-VAE · AWA2H: 63.9 -> 65.6Helped
add feature disentanglement (ablation)CADA-VAE · SUNH: 40.6 -> 41.1Helped
add batch re-combining to the disentangled model (ablation)Disentangled-VAE · CUBH: 53.7 -> 54.4Helped
add batch re-combining to the disentangled model (ablation)Disentangled-VAE · AWA1H: 65.0 -> 66.2Helped
add batch re-combining to the disentangled model (ablation)Disentangled-VAE · AWA2H: 65.6 -> 66.6Helped
add batch re-combining to the disentangled model (ablation)Disentangled-VAE · SUNH: 41.1 -> 41.4Helped
use only distilling latent features instead of only dispersing latent features for classification (ablation)Disentangled-VAE · CUBH: 47.7 -> 54.4Helped
use only distilling latent features instead of only dispersing latent features for classification (ablation)Disentangled-VAE · AWA1H: 54.1 -> 66.2Helped
use only distilling latent features instead of only dispersing latent features for classification (ablation)Disentangled-VAE · AWA2H: 52.4 -> 66.6Helped
use only distilling latent features instead of only dispersing latent features for classification (ablation)Disentangled-VAE · SUNH: 32.9 -> 41.4Helped

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