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Exponential Family Estimation via Adversarial Dynamics Embedding

NeurIPS 2019 · Image generation › GANs · approach: Generative adversarial network theory · 13 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use ADE instead of CD-15synthetic 2spirals estimation · 2spiralsMMD × 1e^-3: -0.45 -> -0.61Helped
use ADE instead of CD-15synthetic Banana estimation · BananaMMD × 1e^-3: -0.31 -> -0.99Helped
use ADE instead of CD-15synthetic circles estimation · circlesMMD × 1e^-3: -0.83 -> -1.13Helped
use ADE instead of NFsynthetic cos estimation · cosMMD × 1e^-3: 0.91 -> -0.55Helped
use ADE instead of CD-15synthetic Cosine estimation · CosineMMD × 1e^-3: 0.78 -> -1.09Helped
use ADE instead of NFsynthetic Funnel estimation · FunnelMMD × 1e^-3: -0.92 -> -0.75Hurt
use ADE instead of CD-15synthetic swissroll estimation · swissrollMMD × 1e^-3: 0.20 -> -0.36Helped
use ADE instead of NFsynthetic line estimation · lineMMD × 1e^-3: 0.39 -> -1.30Helped
use ADE instead of NFsynthetic moons estimation · moonsMMD × 1e^-3: 0.80 -> -1.10Helped
use ADE instead of CD-15synthetic Multiring estimation · MultiringMMD × 1e^-3: -0.38 -> -1.02Helped
use ADE instead of CD-15synthetic pinwheel estimation · pinwheelMMD × 1e^-3: -1.03 -> -0.95Hurt
use ADE instead of CD-15synthetic Ring estimation · RingMMD × 1e^-3: 0.12 -> -0.91Helped

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