mlsanityData to 7 Oct 2026

Adversarially Learned Inference

arXiv 1606.00704 ↗First posted 2 Jun 2016stat.ML cs.LGcited by 216

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Abstract (arXiv)

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.

Key facts. Cited by 216 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Jun 2016.

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Citing papers per year: 2020: 1; 2021: 90; 2022: 54; 2023: 43; 2024: 15; 2025: 10; 2026: 3.

216 papers.

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