Comments (2)
jsyoon0823
commented on July 17, 2024
Yes. TimeGAN is originally designed for unlabeled data as many other generative models are focused on.
I think we can extend TimeGAN for labeled data like how GAN is extended to conditional GAN.
I think there are multiple ways to do this. In addition to what you mentioned above (using labels as features), I think you can also utilize the idea of conditional GAN.
- Using labels as the condition of the generator and discriminator can be another approach for this.
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Nasrin15
commented on July 17, 2024
@AnkurDebnath35 Hey were you able to implement TimeGAN with labelled data? I am trying to do so and I need help.
Thanks in advance.
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Related Issues (20)
- How to search for optimal hyperparameters with TimeGAN (continued)? HOT 1
- Supervised Loss HOT 2
- version of python HOT 1
- About the form of the generated data HOT 6
- About the discriminator HOT 1
- A question about evaluating the predictive score HOT 1
- Question about reconstruction of generated sample HOT 1
- How to apply 1-dimensional data to the timegan framework HOT 4
- Reducing dimensionality with np.mean() HOT 2
- On "Mix the datasets (to make it similar to i.i.d)" HOT 1
- Saving Model During Training and Using Generator Independently of Training HOT 1
- `G_loss_S` does not depend on the generator variables `g_vars`, no need to add it to the solver HOT 1
- The usage of 2 embedder losses (`E_loss_T0` and `E_loss`) HOT 1
- timeGAN 1D HOT 2
- Flipping the data in dataloading.py HOT 1
- identify synthetic data HOT 1
- Timegan outputs are identical HOT 2
- Poor performance on Energy dataset HOT 1
- TimeGAN cannot Renormalization properly during data restoration because Min_Max_scaler is used both in data_loading.py and in timegan.py.
- Tensorflow 2 version ? HOT 1
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