Papers › Adversarially Learned Inference

Adversarially Learned Inference

2 Jun 2016arXiv:1606.00704archive 2025-07-28

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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IshmaelBelghazi/ALI officialmentioned in paperMIT report
9310gaurav/ali-pytorch mentioned on GitHubpytorch report
caotians1/OD-test-master mentioned on GitHubpytorchMIT report
kryvosheyev/xray-anomaly-detection mentioned on GitHubpytorch report
lkhphuc/Anomaly-BiGAN mentioned on GitHubpytorch report
lkhphuc/Anomaly-XRay-GANs mentioned on GitHubpytorch report
pavasgdb/Anomaly-detector-using-GAN mentioned on GitHubpytorch report
zhenxuan00/graphical-gan mentioned on GitHubtf report

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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.

1ran · honoured contract
1ran · our draft was wrong
2unverified

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get_key_from_val IshmaelBelghazi/ALI/ali/mixture_viz.py official repository unverified MIT (permissive) · c9895e295f5b3d66 · report
get_random_uniform_batch 9310gaurav/ali-pytorch/test_semisup.py community (archive-listed) ran · our draft was wrong no licence file found · pointer only · 0fb3b06d95c005bf · report
sample_latent MaximeVandegar/Papers-in-100-Lines-of-Code/Adversarially_Learned_Inference/ali.py community (archive-listed) ran · honoured contract MIT (permissive) · 1422451452fe36c8 · report
tocuda 9310gaurav/ali-pytorch/test_semisup.py community (archive-listed) unverified no licence file found · pointer only · c59ce90932b89fbe · report

Tasks

Image GenerationImage-to-Image Translation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
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

1x1 ConvolutionALIAdamConvolutionMaxoutSVMSigmoid Activation

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