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Dendrogram distance: an evaluation metric for generative networks using hierarchical clustering

arXiv 2311.16894 ↗First posted 28 Nov 2023cs.LG cs.CVcited by 0

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Abstract (arXiv)

We present a novel metric for generative modeling evaluation, focusing primarily on generative networks. The method uses dendrograms to represent real and fake data, allowing for the divergence between training and generated samples to be computed. This metric focus on mode collapse, targeting generators that are not able to capture all modes in the training set. To evaluate the proposed method it is introduced a validation scheme based on sampling from real datasets, therefore the metric is evaluated in a controlled environment and proves to be competitive with other state-of-the-art approaches.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 31 references that are in the map. First posted on arXiv 28 Nov 2023.

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19 of 31 references.

References · 19

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7 more
  1. 2016Mode Regularized Generative Adversarial Networkscited by 123
  2. 2018An empirical study on evaluation metrics of generative adversarial networkscited by 81
  3. 2020Sketchformer: Transformer-based Representation for Sketched Structurecited by 78
  4. 2017DeLiGAN : Generative Adversarial Networks for Diverse and Limited Datacited by 77
  5. 2018How good is my GAN?cited by 74
  6. 2019Quality Aware Generative Adversarial Networkscited by 10
  7. 2021Training Deep Networks from Zero to Hero: avoiding pitfalls and going beyondcited by 3

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