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Towards Bridging the Performance Gaps of Joint Energy-Based Models

CVPR 2023 · Image generation › Autoregressive and latent generation · approach: Generalizable fake image detection · 9 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add SAM to JEM (ablation)JEM · CIFAR10accuracy: 89.5 -> 90.1Helped
Add SAM to JEM (ablation)JEM · CIFAR10FID: 36.2 -> 35.0Helped
Add SAM to JEM++ (ablation)JEM++ · CIFAR10accuracy: 93.5 -> 94.1Helped
Add SAM to JEM++ (ablation)JEM++ · CIFAR10FID: 37.1 -> 36.6Helped
Remove DA from the likelihood pipeline (ablation)JEM++ · CIFAR10FID: 37.1 -> 12.9Helped
Add SAM after removing DA from the likelihood pipeline (ablation)JEM++ w/o DA · CIFAR10accuracy: 93.6 -> 96.0Helped
Add SAM after removing DA from the likelihood pipeline (ablation)JEM++ w/o DA · CIFAR10FID: 12.9 -> 11.4Helped
Replace SAM with energy L2 regularization (ablation)JEM++ w/o DA · CIFAR10accuracy: 96.0 -> 93.4Hurt
Use a larger SGLD sampling-step count K (ablation)SADA-JEM · CIFAR10FID: 9.41 -> 11.4 -> 13.1Hurt
Use a larger SGLD sampling-step count K (ablation)SADA-JEM · CIFAR10accuracy: 95.5 -> 96.0 -> 96.1Helped
Use SAM with rho=0.8 instead of rho=0.2 (ablation)SADA-JEM · CIFAR10accuracy: 96.0 -> 91.9Hurt
Use SAM with rho=0.8 instead of rho=0.2 (ablation)SADA-JEM · CIFAR10FID: 11.4 -> 19.5Hurt

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