Research map / Image generation / GANs
IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use IntroVAE instead of PGGAN | Image synthesis · CelebA | MS-SSIM: 0.2828 -> 0.2719 | Helped |
| Use IntroVAE instead of PGGAN | Image synthesis · LSUN BEDROOM | MS-SSIM: 0.0636 -> 0.0532 | Helped |
| Use IntroVAE instead of PGGAN | Image synthesis · CelebA-HQ | FID: 7.30 -> 5.19 | Helped |
| Use IntroVAE instead of PGGAN | Image synthesis · LSUN BEDROOM | FID: 8.34 -> 8.84 | Hurt |
Closest papers by meaning
- Soft-IntroVAE: Analyzing and Improving the Introspective Variational Autoencoder (CVPR 2021 · similarity 0.94)
- Neural Photo Editing with Introspective Adversarial Networks (ICLR 2017 · similarity 0.90)
- IntroVNMT: An Introspective Model for Variational Neural Machine Translation (AAAI 2020 · similarity 0.89)
- Generating Diverse High-Fidelity Images with VQ-VAE-2 (NeurIPS 2019 · similarity 0.88)
- PixelVAE: A Latent Variable Model for Natural Images (ICLR 2017 · similarity 0.88)
- EQ-VAE: Equivariance Regularized Latent Space for Improved Generative Image Modeling (ICML 2025 · similarity 0.86)
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