Methods › Computer Vision › Generative Models › ALI
Adversarially Learned Inference
ALI
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Adversarially Learned Inference (ALI) is a generative modelling approach that casts the learning of both an inference machine (or encoder) and a deep directed generative model (or decoder) in an GAN-like adversarial framework. A discriminator is trained to discriminate joint samples of the data and the corresponding latent variable from the encoder (or approximate posterior) from joint samples from the decoder while in opposition, the encoder and the decoder are trained together to fool the discriminator. Not is the discriminator asked to distinguish synthetic samples from real data, but it is required it to distinguish between two joint distributions over the data space and the latent variables.
An ALI differs from a GAN in two ways:
- The generator has two components: the encoder, G_z(𝐱), which maps data samples x to z-space, and the decoder Gₓ(𝐳), which maps samples from the prior p(𝐳) (a source of noise) to the input space.
- The discriminator is trained to distinguish between joint pairs (𝐱, 𝐳̃ = G_𝐱(𝐱)) and (𝐱̃ = Gₓ(𝐳), 𝐳), as opposed to marginal samples 𝐱 ∼q(𝐱) and 𝐱̃ ∼ p(𝐱).
Code snippet in the archive: a link on medium.com (archive link, not checked and not linked: not a code host this site links to).
Papers archive 2025-07-28
10 shown of 10, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Smaller But Better: Unifying Layout Generation with Smaller Large Language Models 19 Feb 2025 · 1 repository · arXiv:2502.14005
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Disentangling stellar atmospheric parameters in astronomical spectra using Generative Adversarial Neural Networks 20 Jan 2025 · 0 repositories · arXiv:2501.11762
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Mind the Gap: a Spectral Analysis of Rank Collapse and Signal Propagation in Attention Layers 10 Oct 2024 · 0 repositories · arXiv:2410.07799
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Testing Large Language Models on Driving Theory Knowledge and Skills for Connected Autonomous Vehicles 24 Jul 2024 · 0 repositories · arXiv:2407.17211
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Towards Complete Causal Explanation with Expert Knowledge 10 Jul 2024 · 1 repository · arXiv:2407.07338
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Application of Machine Learning Optimization in Cloud Computing Resource Scheduling and Management 27 Feb 2024 · 0 repositories · arXiv:2402.17216
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Worst-Case Morphs using Wasserstein ALI and Improved MIPGAN 12 Oct 2023 · 0 repositories · arXiv:2310.08371
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Generalized Adversarially Learned Inference 15 Jun 2020 · 0 repositories · arXiv:2006.08089
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The Information-Autoencoding Family: A Lagrangian Perspective on Latent Variable Generative Modeling 1 Jan 2018 · 0 repositories
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Adversarially Learned Inference 2 Jun 2016 · 9 repositories · arXiv:1606.00704Syntology ran 2 of 4 samples · 2 unverified · 2 pointer-only (licence)
Tasks archive 2025-07-28
11 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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