A curated list of time series augmentation and related resources.
Please feel free to pull requests or open an issue to add papers.
Type | T |
GAN |
TA |
`` | `` | `` | Other |
---|---|---|---|---|---|---|---|
Explanation | Traditional method | GAN-based method | Text Augmentation | other types |
Title | Venue | Type | Code | Star |
---|---|---|---|---|
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient | AAAI | GAN |
PyTorch(Author) | 1.9k |
Title | Venue | Type | Code | Star |
---|---|---|---|---|
Data Augmentation for Time Series Classification using Convolutional Neural Networks | ECML-W | T |
PyTorch(Author) |
Title | Venue | Type | Code | Star |
---|---|---|---|---|
Semi-Supervised Time Series Classification | SIGKDD | PyTorch(Author) |
Title | Date | Type | Code | Star |
---|---|---|---|---|
Tabular GANs for Uneven Distribution | 2020.08.10 | - | ||
SeismoGen: Seismic Waveform Synthesis UsingGenerative Adversarial Networks | 2020.05.02 | - | ||
Statistical analysis of Wasserstein GANswith applications to time seriesforecasting | 2020.11.05 | - |
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Time Series Data Augmentation for Deep Learning: A Survey, 2021 arXiv
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An empirical survey of data augmentation for time series classification with neural networks, 2021 arXiv
- ExperienceAI: GAN for Time Series Data Augmentation in Astronomy - Pavlos Protopapas, Harvard U
- Generation of Synthetic Financial Time Series with GANs - Casper Hogenboom
- TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks
- DAGAN
- Data Augmentation GAN in PyTorch
- Data Augmentation optimized for GAN (DAG) - Official implementation
- Data-Efficient GANs with DiffAugment
- StyleGAN2 with adaptive discriminator augmentation (ADA) — Official TensorFlow implementation
- Semantic preserving image-to-image translation
- Resources and Implementations of Generative Adversarial Nets: GAN, DCGAN, WGAN, CGAN, InfoGAN
- InfoGAN-TensorFlow
- InfoGAN-PyTorch
- infoGAN-pytorch
- Progressive InfoGAN
- TSGAN - TimeSeries - GAN
- semisupervised_timeseries_infogan
- Generative Adversarial Nets for Synthetic Time Series Data
- Conditional Generative Adversarial Nets, 2014 arXiv
- InfoGAN: Interpretable Representation Learning byInformation Maximizing Generative Adversarial Nets, 2016 NIPS
- Progressive Growing of GANs for Improved Quality, Stability, and Variation, 2018 ICLR
- Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, 2016 ICLR
- f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization, 2016 NIPS
- Wasserstein GAN, 2017 arXiv
- Improved Training of Wasserstein GANs, 2017 NeurIPS
- Multi-marginal Wasserstein GAN, 2019 NeurIPS
- Differentiable Augmentation for Data-Efficient GAN Training, 2020 NeurIPS
- Training Generative Adversarial Networks with Limited Data, 2020 NeurIPS
- Effective Data Augmentation with Multi-Domain Learning GANs, 2020 AAAI
- On Data Augmentation for GAN Training, 2020 TIP
- Progressive Augmentation of GANs, 2019 NeurIPS
- A survey on Image Data Augmentation for Deep Learning, 2019 JBD
- GAN Augmentation: Augmenting Training Datausing Generative Adversarial Networks, 2018 arXiv
- Data Augmentation Generative Adversarial Networks, 2017 arXiv
- The Effectiveness of Data Augmentation in Image Classification using Deep Learning, 2017 arXiv
- Unpaired Image-to-Image Translationusing Cycle-Consistent Adversarial Networks, 2017 ICCV
- Image-Image Domain Adaptation with Preserved Self-Similarity andDomain-Dissimilarity for Person Re-identification, 2018 CVPR
- AugGAN: Cross Domain Adaptation withGAN-based Data Augmentation, 2018 ECCV
- Connecting Generative Adversarial Networks and Actor-Critic Methods, 2016
- Generative Adversarial Text to Image Synthesis, 2016 ICML
- Professor Forcing: A New Algorithm for Training Recurrent Networks, 2016 NIPS
- Generating Text via Adversarial Training, 2016 NIPS-W
- GANS for Sequences of Discrete Elementswith the Gumbel-softmax Distribution, 2016 NIPS-W
- Adver-sarial Learning for Neural Dialogue Generation, 2017 arXiv
- A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models, 2017 SIGIR