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Learning Deep Latent Variable Models by Short-Run MCMC Inference With Optimal Transport Correction

CVPR 2021 · Probabilistic and scientific modeling › Variational and generative sampling · approach: Sequential Monte Carlo for inference · 6 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add OT correction to short-run MCMC inferencedeep latent variable model · MNIST, SVHN, CelebAMSE: 0.015 -> 0.0008; 0.008 -> 0.002; 0.013 -> 0.010Helped
add OT correction to short-run MCMC inferencedeep latent variable model · MNIST, SVHN, CelebAFID: 19.21 -> 14.28; 29.44 -> 19.48; 37.87 -> 29.75Helped
add OT correction to short-run MCMC inferencedeep latent variable model · MNIST anomaly detection, held-out digits 1, 4, 5, 7, 9AUPRC: 0.336± 0.008 -> 0.353±0.021; 0.630± 0.017 -> 0.770± 0.024; 0.619± 0.013 -> 0.726±0.030; 0.463± 0.009 -> 0.550±0.013; 0.4Helped
increase latent dimension from 20 to 40 to 64 (ablation)proposed model on SVHN · SVHNMSE: 0.011 -> 0.008 -> 0.002Helped
increase latent dimension from 20 to 40 to 64 (ablation)proposed model on SVHN · SVHNFID: 36.32 -> 24.73 -> 19.48Helped
apply OT correction (ablation)proposed model with Langevin step size s=3e-2 · SVHNFID: 22.70 -> 21.89Helped
apply OT correction (ablation)proposed model with Langevin step size s=3e-2 · SVHNMSE: 0.011 -> 0.013Hurt
use an excessively large Langevin step size s=6e-2 (ablation)proposed model · SVHNFID: 109.97 before -> 87.77 afterHurt
increase Langevin steps from 30 to 50 to 100 (ablation)proposed model · SVHNMSE: 0.014 -> 0.011 -> 0.007Helped
increase Langevin steps from 30 to 50 to 100 (ablation)proposed model · SVHNFID: 22.32 -> 18.57 -> 15.43Helped
increase learning-step gradient-ascent iterations from 1 to 2 to 3 (ablation)proposed model · SVHNMSE: 0.013 -> 0.010 -> 0.008Helped
increase learning-step gradient-ascent iterations from 1 to 2 to 3 (ablation)proposed model · SVHNFID: 21.89 -> 17.32 -> 14.28Helped

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