Comments (3)
ty5491003
commented on August 17, 2024
Closed.
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weilinie
commented on August 17, 2024
To meet the syntax specification, we need a so-called controllable generation, in which a naive conditional RelGAN may not be sufficient. As far as I know, it is generally difficult to control the output of GANs and many recent works have been focused on it.
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ty5491003
commented on August 17, 2024
I get it, thx!
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Related Issues (20)
- NLL_gen loss with initialization HOT 14
- Where generated text is being stored? HOT 1
- How much time it took to run a single cycle of 5 discriminator steps and 1 generator step during the adversarial training? HOT 3
- Overfitting during Pretraining HOT 3
- AttributeError: module 'tensorflow' has no attribute 'truncated_normal_initializer' HOT 1
- Teacher Forcing during Adversarial Training HOT 1
- Facing the error HOT 8
- Why Gumbel-Softmax relaxation applied in sample generation? HOT 4
- Dis loss decays to zero while training oracle HOT 3
- How do i train the model with my own data? HOT 2
- Loss function for LSTM based model HOT 1
- LICENSE? HOT 2
- Questions on reproducing the results HOT 18
- How long is expected to take to complete the pre-training stage (on single GPU) ? HOT 4
- Thank you for your contribution. Can you briefly explain how to run and validate your model? HOT 1
- Improving sample output quality RELGAN. HOT 2
- my idea of metric on diversity. HOT 6
- [Self-BLEU] what metric values do you get? HOT 4
- Using RelGAN for paraphrase generation HOT 1
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