Research map / Image generation / Fast diffusion sampling
Soft-IntroVAE: Analyzing and Improving the Introspective Variational Autoencoder
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace IntroVAE's hard-margin hinge loss with a soft exponential ELBO penalty | IntroVAE · 8 Gaussians | KL: 2.53$\pm$1.07 -> 1.25$\pm$0.11 | Helped |
| Replace IntroVAE's hard-margin hinge loss with a soft exponential ELBO penalty | IntroVAE · Spiral | KL: 8.38$\pm$0.45 -> 8.13$\pm$0.3 | Helped |
| Replace IntroVAE's hard-margin hinge loss with a soft exponential ELBO penalty | IntroVAE · Checkerboard | KL: 19.03$\pm$0.34 -> 20.27$\pm$0.21 | Hurt |
| Replace IntroVAE's hard-margin hinge loss with a soft exponential ELBO penalty | IntroVAE · Rings | KL: 10.21$\pm$0.49 -> 9.18$\pm$0.33 | Helped |
| Replace IntroVAE's hard-margin hinge loss with a soft exponential ELBO penalty | IntroVAE · Checkerboard | gnELBO: 8.54$\pm$0.11 -> 4.47$\pm$0.29 | Helped |
| Use class conditioning | Soft-IntroVAE · CIFAR-10 | FID: 4.6 -> 4.07 | Helped |
| Use unsupervised Soft-IntroVAE content transfer instead of supervised LORD | Cars3D content transfer · Cars3D | LPIPS: 0.078 -> 0.084 | Hurt |
Closest papers by meaning
- IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis (NeurIPS 2018 · similarity 0.94)
- Adversarial Symmetric Variational Autoencoder (NeurIPS 2017 · similarity 0.89)
- IntroVNMT: An Introspective Model for Variational Neural Machine Translation (AAAI 2020 · similarity 0.89)
- NVAE: A Deep Hierarchical Variational Autoencoder (NeurIPS 2020 · similarity 0.89)
- Beyond Vanilla Variational Autoencoders: Detecting Posterior Collapse in Conditional and Hierarchical Variational Autoencoders (ICLR 2024 · similarity 0.88)
- Sparse Autoencoders, Again? (ICML 2025 · similarity 0.87)
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