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Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting

NeurIPS 2019 · Probabilistic and scientific modeling › Variational and generative sampling · approach: Score-based generative modeling analysis · 65 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Apply self-normalized LFIW to PixelCNN++ samplesPixelCNN++ · CIFAR-10IS: 5.16 ± 0.0117 -> 6.68 ± 0.0773Helped
Apply self-normalized LFIW to PixelCNN++ samplesPixelCNN++ · CIFAR-10FID: 58.70 ± 0.0506 -> 55.83 ± 0.9695Helped
Apply self-normalized LFIW to SNGAN samplesSNGAN · CIFAR-10KID: 0.094 ± 0.0002 -> 0.073 ± 0.0004Helped
Importance-weight generated points in the real-plus-generated training setDAGAN augmentation classifier · Omniglotaccuracy: 0.6600 ± 0.0040 -> 0.6818 ± 0.0022Helped
Use generated training points without LFIW (ablation)DAGAN augmentation classifier · Omniglotaccuracy: 0.6603 ± 0.0012 -> 0.6600 ± 0.0040No clear effect
Weight simulated trajectories with LFIWLearned dynamics model · Swimmerpolicy return: 100.4 ± 3.2 -> 25.7 ± 3.1Mixed
Weight simulated trajectories with LFIWLearned dynamics model · HalfCheetahpolicy return: 204.0 ± 0.8 -> 217.8 ± 4.0Helped
Weight simulated trajectories with LFIWLearned dynamics model · HumanoidStanduppolicy return: 8417 ± 28 -> 9372 ± 375Helped
Use flattening with α=0.25 instead of self-normalized weighting (ablation)SNGAN LFIW · CIFAR-10 Inception activationsMSE: 0.0016 ± 0.00023 -> 0.0011 ± 8e-05Helped
Use flattening with α=0.25 instead of self-normalized weighting (ablation)PixelCNN++ LFIW · CIFAR-10 Inception activationsMSE: 0.0046 ± 0.00031 -> 0.0175 ± 0.00138Hurt
Clip importance weights at lower bound β=1.0 (ablation)SNGAN LFIW · CIFAR-10 Inception activationsMSE: 0.0016 ± 0.00023 -> 0.0170 ± 0.00171Hurt
Clip importance weights at lower bound β=0.1 (ablation)PixelCNN++ LFIW · CIFAR-10 Inception activationsMSE: 0.0046 ± 0.00031 -> 0.0215 ± 0.00149Hurt

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