Papers › Adversarially Learned Inference
Adversarially Learned Inference
Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Olivier Mastropietro, Alex Lamb, Martin Arjovsky, Aaron Courville
We introduce the adversarially learned inference (ALI) model, which jointly learns a generation network and an inference network using an adversarial process. The generation network maps samples from stochastic latent variables to the data space while the inference network maps training examples in data space to the space of latent variables. An adversarial game is cast between these two networks and a discriminative network is trained to distinguish between joint latent/data-space samples from the generative network and joint samples from the inference network. We illustrate the ability of the model to learn mutually coherent inference and generation networks through the inspections of model samples and reconstructions and confirm the usefulness of the learned representations by obtaining a performance competitive with state-of-the-art on the semi-supervised SVHN and CIFAR10 tasks.
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Code
Syntology Ran 2 of 4 code samples harvested from 3 repositories linked to this paper; 2 have no recorded run. Of those that ran: 1 ran · honoured contract; 1 ran · our draft was wrong.
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Code Syntology ran Syntology
4 samples harvested; 2 ran; 1 honoured the contract we drafted; 2 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.
Licence: 2 of the 4 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image-to-Image Translation | Cityscapes Labels-to-Photo | BiGAN | Class IOU | 0.02 | #20 of 21 | Archive leaderboard | report |
| Image-to-Image Translation | Cityscapes Labels-to-Photo | BiGAN | Per-class Accuracy | 6% | #20 of 21 | Archive leaderboard | report |
| Image-to-Image Translation | Cityscapes Labels-to-Photo | BiGAN | Per-pixel Accuracy | 19% | #20 of 21 | Archive leaderboard | report |
| Image-to-Image Translation | Cityscapes Photo-to-Labels | BiGAN | Class IOU | 0.07 | #4 of 5 | Archive leaderboard | report |
| Image-to-Image Translation | Cityscapes Photo-to-Labels | BiGAN | Per-class Accuracy | 13% | #4 of 5 | Archive leaderboard | report |
| Image-to-Image Translation | Cityscapes Photo-to-Labels | BiGAN | Per-pixel Accuracy | 41% | #4 of 5 | 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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