Methods › Computer Vision › Generative Discrimination › Minibatch Discrimination

Minibatch Discrimination

5 papers tagged archive 2025-07-28

Introduced by Tim Salimans et al. in Improved Techniques for Training GANs

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

Minibatch Discrimination is a discriminative technique for generative adversarial networks where we discriminate between whole minibatches of samples rather than between individual samples. This is intended to avoid collapse of the generator.

PaperSource

Papers archive 2025-07-28

5 shown of 5, 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

9 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 Generation2
Aspect-Based Sentiment Analysis1
Aspect-Based Sentiment Analysis (ABSA)1
Conditional Image Generation1
Contrastive Learning1
Disaster Response1
Semi-Supervised Image Classification1
Sentence1
Sentiment Analysis1

Usage over time archive 2025-07-28

Papers per year tagged with Minibatch Discrimination: 2016 to 2024, peak 2 2 0 2016: 2 papers 2016 2017: 0 papers 2017 2018: 0 papers 2018 2019: 0 papers 2019 2020: 0 papers 2020 2021: 1 paper 2021 2022: 0 papers 2022 2023: 1 paper 2023 2024: 1 paper 2024
Papers per year the archive tags with this method, by the paper's archive date (5 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 Discrimination

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