mlsanityData to 14 Oct 2026

Shared Loss between Generators of GANs

arXiv 2211.07234 ↗First posted 14 Nov 2022cs.LG cs.AIcited by 0

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Abstract (arXiv)

Generative adversarial networks are generative models that are capable of replicating the implicit probability distribution of the input data with high accuracy. Traditionally, GANs consist of a Generator and a Discriminator which interact with each other to produce highly realistic artificial data. Traditional GANs fall prey to the mode collapse problem, which means that they are unable to generate the different variations of data present in the input dataset. Recently, multiple generators have been used to produce more realistic output by mitigating the mode collapse problem. We use this multiple generator framework. The novelty in this paper lies in making the generators compete against each other while interacting with the discriminator simultaneously. We show that this causes a dramatic reduction in the training time for GANs without affecting its performance.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 25 references that are in the map. First posted on arXiv 14 Nov 2022.

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References · 23

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11 more
  1. 2016Mode Regularized Generative Adversarial Networkscited by 123
  2. 2015How (not) to Train your Generative Model: Scheduled Sampling, Likelihood, Adversary?cited by 78
  3. 2016Generative Multi-Adversarial Networkscited by 60
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  7. 2021SHARKS: Smart Hacking Approaches for RisK Scanning in Internet-of-Things and Cyber-Physical Systems based on Machine Learningcited by 4
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  11. 2021Machine Learning Assisted Security Analysis of 5G-Network-Connected Systemscited by 2

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