Papers › Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks...
Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks via Singular Values
Ahmed Imtiaz Humayun, Randall Balestriero, Richard Baraniuk
We present Polarity Sampling, a theoretically justified plug-and-play method for controlling the generation quality and diversity of pre-trained deep generative networks DGNs). Leveraging the fact that DGNs are, or can be approximated by, continuous piecewise affine splines, we derive the analytical DGN output space distribution as a function of the product of the DGN's Jacobian singular values raised to a power ρ. We dub ρ the polarity parameter and prove that ρ focuses the DGN sampling on the modes (ρ< 0) or anti-modes (ρ> 0) of the DGN output-space distribution. We demonstrate that nonzero polarity values achieve a better precision-recall (quality-diversity) Pareto frontier than standard methods, such as truncation, for a number of state-of-the-art DGNs. We also present quantitative and qualitative results on the improvement of overall generation quality (e.g., in terms of the Frechet Inception Distance) for a number of state-of-the-art DGNs, including StyleGAN3, BigGAN-deep, NVAE, for different conditional and unconditional image generation tasks. In particular, Polarity Sampling redefines the state-of-the-art for StyleGAN2 on the FFHQ Dataset to FID 2.57, StyleGAN2 on the LSUN Car Dataset to FID 2.27 and StyleGAN3 on the AFHQv2 Dataset to FID 3.95. Demo: bit.ly/polarity-samp
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Generation | AFHQV2 | Polarity-StyleGAN3 | FID | 3.95 | #1 of 7 | Archive leaderboard | report |
| Image Generation | CelebA-HQ 1024x1024 | Polarity-ProGAN | FID | 7.28 | #6 of 10 | Archive leaderboard | report |
| Image Generation | FFHQ 1024 x 1024 | Polarity-StyleGAN2 | FID | 2.57 | #3 of 20 | Archive leaderboard | report |
| Image Generation | ImageNet 256x256 | Polarity-BigGAN | FID | 6.82 | #91 of 94 | Archive leaderboard | report |
| Image Generation | LSUN Car 512 x 384 | Polarity-StyleGAN2 | FID | 2.27 | #1 of 2 | Archive leaderboard | report |
| Image Generation | LSUN Cat 256 x 256 | Polarity-StyleGAN2 | FID | 6.34 | #4 of 8 | Archive leaderboard | report |
| Image Generation | LSUN Churches 256 x 256 | Polarity-StyleGAN2 | FID | 3.92 | #11 of 27 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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