{"url":"/method/ali","slug":"ali","name":"ALI","full_name":"Adversarially Learned Inference","full_name_withheld":false,"description_markdown":"**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.\r\n\r\nAn ALI differs from a [GAN](https://paperswithcode.com/method/gan) in two ways:\r\n\r\n- The generator has two components: the encoder, $G\\_{z}\\left(\\mathbf{x}\\right)$, which maps data samples $x$ to $z$-space, and the decoder $G\\_{x}\\left(\\mathbf{z}\\right)$, which maps samples from the prior $p\\left(\\mathbf{z}\\right)$ (a source of noise) to the input space.\r\n- The discriminator is trained to distinguish between joint pairs $\\left(\\mathbf{x}, \\tilde{\\mathbf{z}} = G\\_{\\mathbf{x}}\\left(\\mathbf{x}\\right)\\right)$ and $\\left(\\tilde{\\mathbf{x}} =\r\nG\\_{x}\\left(\\mathbf{z}\\right), \\mathbf{z}\\right)$, as opposed to marginal samples $\\mathbf{x} \\sim q\\left(\\mathbf{x}\\right)$ and $\\tilde{\\mathbf{x}} ∼ p\\left(\\mathbf{x}\\right)$.","description_state":"present","introduced_year":null,"introduced_by":{"title":null,"paper":null,"first_author":null,"n_authors":0,"url_abs":null,"archive_paper_url":null},"source":{"url":"http://arxiv.org/abs/1606.00704v3","title":"Adversarially Learned Inference","url_on_a_paper_host":true},"code_snippet_url":"https://medium.com/@zulfikar.zahedi/ali-zulfikar-zahedi-the-filmmaker-entrant-from-bangladesh-ready-to-stun-bollywood-4321b91cdced","code_snippet_url_on_a_code_host":false,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Generative Models","url":"/methods/category/generative-models","pwc_aliases":[]}],"n_papers_tagged":10,"archive_num_papers":null,"papers_newest_first":[{"paper":"/paper/smaller-but-better-unifying-layout-generation","title":"Smaller But Better: Unifying Layout Generation with Smaller Large Language Models","date":"2025-02-19","arxiv_id":"2502.14005","n_code_links":1,"syntology":null},{"paper":null,"title":"Disentangling stellar atmospheric parameters in astronomical spectra using Generative Adversarial Neural Networks","date":"2025-01-20","arxiv_id":"2501.11762","n_code_links":0,"syntology":null},{"paper":null,"title":"Mind the Gap: a Spectral Analysis of Rank Collapse and Signal Propagation in Attention Layers","date":"2024-10-10","arxiv_id":"2410.07799","n_code_links":0,"syntology":null},{"paper":null,"title":"Testing Large Language Models on Driving Theory Knowledge and Skills for Connected Autonomous Vehicles","date":"2024-07-24","arxiv_id":"2407.17211","n_code_links":0,"syntology":null},{"paper":"/paper/towards-complete-causal-explanation-with","title":"Towards Complete Causal Explanation with Expert Knowledge","date":"2024-07-10","arxiv_id":"2407.07338","n_code_links":1,"syntology":null},{"paper":null,"title":"Application of Machine Learning Optimization in Cloud Computing Resource Scheduling and Management","date":"2024-02-27","arxiv_id":"2402.17216","n_code_links":0,"syntology":null},{"paper":null,"title":"Worst-Case Morphs using Wasserstein ALI and Improved MIPGAN","date":"2023-10-12","arxiv_id":"2310.08371","n_code_links":0,"syntology":null},{"paper":null,"title":"Generalized Adversarially Learned Inference","date":"2020-06-15","arxiv_id":"2006.08089","n_code_links":0,"syntology":null},{"paper":null,"title":"The Information-Autoencoding Family: A Lagrangian Perspective on Latent Variable Generative Modeling","date":"2018-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/adversarially-learned-inference","title":"Adversarially Learned Inference","date":"2016-06-02","arxiv_id":"1606.00704","n_code_links":9,"syntology":{"ran":2,"of":4,"unverified":2,"pointer_only":2}}],"papers_shown":10,"tasks":[{"task":"/task/autonomous-driving","name":"Autonomous Driving","papers":1},{"task":"/task/autonomous-vehicles","name":"Autonomous Vehicles","papers":1},{"task":"/task/cloud-computing","name":"Cloud Computing","papers":1},{"task":"/task/dimensionality-reduction","name":"Dimensionality Reduction","papers":1},{"task":"/task/face-recognition","name":"Face Recognition","papers":1},{"task":"/task/image-generation","name":"Image Generation","papers":1},{"task":"/task/image-to-image-translation","name":"Image-to-Image Translation","papers":1},{"task":"/task/layout-generation","name":"Layout Generation","papers":1},{"task":"/task/morph","name":"MORPH","papers":1},{"task":"/task/management","name":"Management","papers":1},{"task":"/task/scheduling","name":"Scheduling","papers":1}],"tasks_shown":11,"n_tasks":11,"usage_by_year":[{"year":"2016","papers":1},{"year":"2018","papers":1},{"year":"2020","papers":1},{"year":"2023","papers":1},{"year":"2024","papers":4},{"year":"2025","papers":2}],"row_source":"embedded","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/ali"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}