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From Inference to Generation: End-to-end Fully Self-supervised Generation of Human Face from Speech

ICLR 2020 · Image generation › Autoregressive and latent generation · approach: Neural text-to-speech synthesis · 13 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add mismatched-identity loss (relidGANs) during generation training (ablation)Face generation from speech · AVSpeech face retrieval, Top-1 cosine distancecosine distance retrieval performance: 7.36 -> 13.59Helped
Add mismatched-identity loss (relidGANs) during generation training (ablation)Face generation from speech · AVSpeech test, generated image versus ground-truth imageinference preference (%): 47.2% -> 76.65%Helped
Add mismatched-identity loss (relidGANs) during generation training (ablation)Face generation from speech · AVSpeech test, comparison of two generated imagesinference preference (%): 79.68%Helped
Add mismatched-identity loss (relidGANs) during generation training (ablation)Face generation from speech · AVSpeech face retrieval, Top-10 cosine distancecosine distance retrieval performance: 39.59 -> 53.83Helped
Skip inference-stage training before training the generator (ablation)Two-stage inference and generation framework · AVSpeech face generationspeech-condition useHurt
Train using self-supervised cross-modal matching rather than supervised identity labelsCross-modal identity matching · VoxCeleb ∩ VGGFace, V-F 10-wayaccuracy (%): 40 -> 55.66Helped
Train using self-supervised cross-modal matching rather than supervised identity labelsCross-modal identity matching · VoxCeleb ∩ VGGFace, V-F 2-wayaccuracy (%): 84.12 -> 79.90Hurt

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