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GAN Hinge Loss

167 papers tagged archive 2025-07-28

Introduced by Jae Hyun Lim et al. in Geometric GAN

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

The GAN Hinge Loss is a hinge loss based loss function for generative adversarial networks:

L_D = -𝔼_((x, y)∼p_(data))[min(0, -1 + D(x, y))] -𝔼_(z∼p_z, y∼p_(data))[min(0, -1 - D(G(z), y))]

L_G = -𝔼_(z∼p_z, y∼p_(data))D(G(z), y)

PaperSourceSee Code · lim0606/pytorch-geometric-gan

Papers archive 2025-07-28

30 shown of 167, 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 168 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 Generation45
Conditional Image Generation17
Generative Adversarial Network16
reinforcement-learning9
Data Augmentation8
Vocal Bursts Intensity Prediction8
CPU7
Multi-agent Reinforcement Learning7
Reinforcement Learning7
Speech Synthesis7
Translation7
Attribute6
GPU6
Image-to-Image Translation6
Reinforcement Learning (RL)6
Super-Resolution6
Unconditional Image Generation6
Decision Making5
Denoising5
Diversity5

Usage over time archive 2025-07-28

Papers per year tagged with GAN Hinge Loss: 2017 to 2024, peak 42 42 0 2017: 2 papers 2017 2018: 6 papers 2018 2019: 14 papers 2019 2020: 42 papers 2020 2021: 26 papers 2021 2022: 36 papers 2022 2023: 38 papers 2023 2024: 3 papers 2024
Papers per year the archive tags with this method, by the paper's archive date (167 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

Loss Functions

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