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Research map / Image generation / Autoregressive and latent generation

A Neural Representation of Sketch Drawings

ICLR 2018 · Image generation › Autoregressive and latent generation · approach: Classical generative image synthesis · 538 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
lower KL weight wKL from 1.00 to 0.25 (ablation)sketch-rnn · catreconstruction loss LR: -0.98 -> -1.46Helped
lower KL weight wKL from 1.00 to 0.25 (ablation)sketch-rnn · catKL loss LKL: 0.29 -> 1.01Hurt
lower KL weight wKL from 1.00 to 0.25 (ablation)sketch-rnn · facereconstruction loss LR: -1.13 -> -1.90Helped
lower KL weight wKL from 1.00 to 0.25 (ablation)sketch-rnn · mosquitoKL loss LKL: 0.30 -> 1.54Hurt
lower KL weight wKL from 1.00 to 0.25 (ablation)sketch-rnn · gardenreconstruction loss LR: -0.79 -> -0.99Helped
lower KL weight wKL from 1.00 to 0.50 (ablation)sketch-rnn · firetruckreconstruction loss LR: -1.24 -> -1.26No clear effect
add latent vector from encoder (conditional vs decoder only) (ablation)sketch-rnn · catreconstruction loss LR: -0.57 -> -0.98Helped
add latent vector from encoder (conditional vs decoder only) (ablation)sketch-rnn · mosquitoreconstruction loss LR: -0.34 -> -0.67Helped
train on multi-class (crab, face, pig, rabbit) instead of single classsketch-rnn · crab, face, pig, rabbitreconstruction loss LR: -0.91 vs -1.14Mixed
lower wKL from 1.00 to 0.25 in the multi-class (cat, pig) dataset (ablation)sketch-rnn · cat, pigKL loss LKL: 0.22 -> 0.98Hurt

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