Methods › Computer Vision › Generative Models › SIG

Sliced Iterative Generator

SIG

17 papers tagged archive 2025-07-28

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

The Sliced Iterative Generator (SIG) is an iterative generative model that is a Normalizing Flow (NF), but shares the advantages of Generative Adversarial Networks (GANs). The model is based on iterative Optimal Transport of a series of 1D slices through the data space, matching on each slice the probability distribution function (PDF) of the samples to the data. To improve the efficiency, the directions of the orthogonal slices are chosen to maximize the PDF difference between the generated samples and the data using Wasserstein distance at each iteration. A patch based approach is adopted to model the images in a hierarchical way, enabling the model to scale well to high dimensions.

Unlike GANs, SIG has a NF structure and allows efficient likelihood evaluations that can be used in downstream tasks. While SIG has a deep neural network architecture, the approach deviates significantly from the current deep learning paradigm, as it does not use concepts such as mini-batching, stochastic gradient descent, gradient back-propagation through deep layers, or non-convex loss function optimization. SIG is very insensitive to hyper-parameter tuning, making it a useful generator tool for ML experts and non-experts alike.

Source: Sliced Iterative Normalizing FlowsSee Code · biweidai/SIG

Papers archive 2025-07-28

17 shown of 17, 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 32 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
Image Generation4
Action Recognition1
Autonomous Navigation1
Benchmarking1
C++ code1
CPU1
Deep Learning1
Density Estimation1
Disentanglement1
Diversity1
Domain Adaptation1
Dynamic Time Warping1
Face Recognition1
Fairness1
GPU1
Image Manipulation1
One-Shot Learning1
Out-of-Distribution Detection1
Position1
Prediction1

Usage over time archive 2025-07-28

Papers per year tagged with SIG: 2020 to 2025, peak 7 7 0 2020: 1 paper 2020 2021: 2 papers 2021 2022: 2 papers 2022 2023: 7 papers 2023 2024: 3 papers 2024 2025: 2 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (17 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

Generative Models

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