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Class Conditional Time Series Generation with Structured Noise Space GAN

arXiv 2312.12946 ↗First posted 20 Dec 2023cs.LG stat.MLcited by 0

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Abstract (arXiv)

This paper introduces Structured Noise Space GAN (SNS-GAN), a novel approach in the field of generative modeling specifically tailored for class-conditional generation in both image and time series data. It addresses the challenge of effectively integrating class labels into generative models without requiring structural modifications to the network. The SNS-GAN method embeds class conditions within the generator's noise space, simplifying the training process and enhancing model versatility. The model's efficacy is demonstrated through qualitative validations in the image domain and superior performance in time series generation compared to baseline models. This research opens new avenues for the application of GANs in various domains, including but not limited to time series and image data generation.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 18 references that are in the map. First posted on arXiv 20 Dec 2023.

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References · 12

  1. 2016Improved Techniques for Training GANscited by 2,970
  2. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,626
  3. 2014Conditional Generative Adversarial Netscited by 2,162
  4. 2018Spectral Normalization for Generative Adversarial Networkscited by 1,636
  5. 2016Conditional Image Synthesis With Auxiliary Classifier GANscited by 565
  6. 2016A Learned Representation For Artistic Stylecited by 341
  7. 2017Real-valued (Medical) Time Series Generation with Recurrent Conditional GANscited by 333
  8. 2021Rebooting ACGAN: Auxiliary Classifier GANs with Stable Trainingcited by 57
  9. 2021Conditional GANs with Auxiliary Discriminative Classifiercited by 20
  10. 2020Omni-GAN: On the Secrets of cGANs and Beyondcited by 11
  11. 2021Dual Projection Generative Adversarial Networks for Conditional Image Generationcited by 8
  12. 2022Quantifying Quality of Class-Conditional Generative Models in Time-Series Domaincited by 1

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