Methods › Computer Vision › Likelihood-Based Generative Models › NICE

Non-linear Independent Component Estimation

NICE

22 papers tagged archive 2025-07-28

Introduced by Laurent Dinh et al. in NICE: Non-linear Independent Components Estimation

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

NICE, or Non-Linear Independent Components Estimation is a framework for modeling complex high-dimensional densities. It is based on the idea that a good representation is one in which the data has a distribution that is easy to model. For this purpose, a non-linear deterministic transformation of the data is learned that maps it to a latent space so as to make the transformed data conform to a factorized distribution, i.e., resulting in independent latent variables. The transformation is parameterised so that computing the determinant of the Jacobian and inverse Jacobian is trivial, yet it maintains the ability to learn complex non-linear transformations, via a composition of simple building blocks, each based on a deep neural network. The training criterion is simply the exact log-likelihood. The transformation used in NICE is the affine coupling layer without the scale term, known as additive coupling layer:

y_(I₂) = x_(I₂) + m(x_(I₁))

x_(I₂) = y_(I₂) + m(y_(I₁))

PaperSourceSee Code · paultsw/nice_pytorch

Papers archive 2025-07-28

22 shown of 22, 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.

Tasks archive 2025-07-28

20 shown of 39 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Causal Inference2
Graph Generation2
Image Captioning2
Scene Graph Generation2
counterfactual2
All1
CPU1
Causal Identification1
Class Incremental Learning1
Clinical Knowledge1
Continual Learning1
Deep Learning1
Diagnostic1
Entity Disambiguation1
Fairness1
Hippocampus1
Image Generation1
Image Registration1
Incremental Learning1
Knowledge Distillation1

Usage over time archive 2025-07-28

Papers per year tagged with NICE: 2014 to 2025, peak 7 7 0 2014: 1 paper 2014 2015: 0 papers 2015 2016: 0 papers 2016 2017: 0 papers 2017 2018: 0 papers 2018 2019: 0 papers 2019 2020: 1 paper 2020 2021: 4 papers 2021 2022: 4 papers 2022 2023: 7 papers 2023 2024: 4 papers 2024 2025: 1 paper 2025
Papers per year the archive tags with this method, by the paper's archive date (22 dated). Bars are counts, not a trend claim.

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

Likelihood-Based Generative ModelsGenerative Models

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