Methods › Computer Vision › Generative Adversarial Networks › LSGAN

LSGAN

18 papers tagged archive 2025-07-28

Introduced by Xudong Mao et al. in Least Squares Generative Adversarial Networks

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

LSGAN, or Least Squares GAN, is a type of generative adversarial network that adopts the least squares loss function for the discriminator. Minimizing the objective function of LSGAN yields minimizing the Pearson χ² divergence. The objective function can be defined as:

min_DV_(LSGAN)(D) = 1/2𝔼_(𝐱 ∼p_(data)(𝐱))[(D(𝐱) - b)²] + 1/2𝔼_(𝐳∼p_𝐳(𝐳))[(D(G(𝐳)) - a)²]

min_GV_(LSGAN)(G) = 1/2𝔼_(𝐳 ∼p_𝐳(𝐳))[(D(G(𝐳)) - c)²]

where a and b are the labels for fake data and real data and c denotes the value that G wants D to believe for fake data.

PaperSourceSee Code · eriklindernoren/PyTorch-GAN

Papers archive 2025-07-28

18 shown of 18, 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 27 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
Face Generation3
Image Generation3
Generative Adversarial Network2
Audio-Visual Synchronization1
Diagnostic1
Dimensionality Reduction1
Diversity1
Fairness1
Image Enhancement1
Image Reconstruction1
Image Segmentation1
Image-to-Image Translation1
Knowledge Distillation1
Low-Light Image Enhancement1
MRI Reconstruction1
Neural Rendering1
Semantic Segmentation1
Speech Synthesis1
Survey1
Synthetic Image Detection1

Usage over time archive 2025-07-28

Papers per year tagged with LSGAN: 2016 to 2022, peak 5 5 0 2016: 1 paper 2016 2017: 2 papers 2017 2018: 4 papers 2018 2019: 1 paper 2019 2020: 5 papers 2020 2021: 4 papers 2021 2022: 1 paper 2022
Papers per year the archive tags with this method, by the paper's archive date (18 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 Adversarial NetworksGenerative Models

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