A TensorFlow Implementation of the Transformer: Attention Is All You Need

Recurrent (conditional) generative adversarial networks for generating real-valued time series data.

Implementation of Sequence Generative Adversarial Nets with Policy Gradient

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[IMC 2020 (Best Paper Finalist)] Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions

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经历BAT面试后总结的【高级Java后台开发面试指南】，纯净干货无废话，针对高频面试点

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[NeurIPS‘2021] "TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up", Yifan Jiang, Shiyu Chang, Zhangyang Wang

TTS-GAN: A Transformer-based Time-Series Generative Adversarial Network

Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.

A model to generate time series data with the purpose of augmenting a dataset of various time series.

A curated list of time series augmentation resources.

Codebase for Time-series Generative Adversarial Networks (TimeGAN) - NeurIPS 2019

Resources and Implementations of Generative Adversarial Nets: GAN, DCGAN, WGAN, CGAN, InfoGAN

The data and code of Keyword-aware Abstractive Summarization by Extracting Set-level Intermediate Summaries (WWW 21')

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