Papers › Generative Latent Flow
Generative Latent Flow
Zhisheng Xiao, Qing Yan, Yali Amit
In this work, we propose the Generative Latent Flow (GLF), an algorithm for generative modeling of the data distribution. GLF uses an Auto-encoder (AE) to learn latent representations of the data, and a normalizing flow to map the distribution of the latent variables to that of simple i.i.d noise. In contrast to some other Auto-encoder based generative models, which use various regularizers that encourage the encoded latent distribution to match the prior distribution, our model explicitly constructs a mapping between these two distributions, leading to better density matching while avoiding over regularizing the latent variables. We compare our model with several related techniques, and show that it has many relative advantages including fast convergence, single stage training and minimal reconstruction trade-off. We also study the relationship between our model and its stochastic counterpart, and show that our model can be viewed as a vanishing noise limit of VAEs with flow prior. Quantitatively, under standardized evaluations, our method achieves state-of-the-art sample quality among AE based models on commonly used datasets, and is competitive with GANs' benchmarks.
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Code
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Code Syntology ran Syntology
5 samples harvested; 4 ran; 0 honoured the contract we drafted; 1 has no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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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 | CIFAR-10 | GLF+perceptual loss (ours) | FID | 44.6 | #74 of 78 | Archive leaderboard | report |
| Image Generation | CelebA 256x256 | GLF+perceptual loss (ours) | FID | 41.8 | #17 of 17 | Archive leaderboard | report |
| Image Generation | Fashion-MNIST | GLF+perceptual loss (ours) | FID | 10.3 | #1 of 7 | Archive leaderboard | report |
| Image Generation | MNIST | GLF+perceptual loss (ours) | FID | 5.8 | #8 of 15 | 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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