Papers › Residual Flows for Invertible Generative Modeling
Residual Flows for Invertible Generative Modeling
Ricky T. Q. Chen, Jens Behrmann, David Duvenaud, Jörn-Henrik Jacobsen
Flow-based generative models parameterize probability distributions through an invertible transformation and can be trained by maximum likelihood. Invertible residual networks provide a flexible family of transformations where only Lipschitz conditions rather than strict architectural constraints are needed for enforcing invertibility. However, prior work trained invertible residual networks for density estimation by relying on biased log-density estimates whose bias increased with the network's expressiveness. We give a tractable unbiased estimate of the log density using a "Russian roulette" estimator, and reduce the memory required during training by using an alternative infinite series for the gradient. Furthermore, we improve invertible residual blocks by proposing the use of activation functions that avoid derivative saturation and generalizing the Lipschitz condition to induced mixed norms. The resulting approach, called Residual Flows, achieves state-of-the-art performance on density estimation amongst flow-based models, and outperforms networks that use coupling blocks at joint generative and discriminative modeling.
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Code
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Code Syntology ran Syntology
8 samples harvested; 5 ran; 1 honoured the contract we drafted; 3 have 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 | Residual Flow | FID | 46.37 | #75 of 78 | Archive leaderboard | report |
| Image Generation | CelebA 256x256 | Residual Flow | bpd | 0.992 | #9 of 17 | Archive leaderboard | report |
| Image Generation | ImageNet 32x32 | Residual Flow | bpd | 4.01 | #32 of 35 | Archive leaderboard | report |
| Image Generation | ImageNet 64x64 | Residual Flow | Bits per dim | 3.757 | #60 of 65 | Archive leaderboard | report |
| Image Generation | MNIST | Residual Flow | bits/dimension | 0.97 | #2 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.
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