SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
Authors: Lantao Yu, Weinan Zhang, Jun Wang, Yong Yu
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on synthetic data and real-world tasks demonstrate significant improvements over strong baselines. and Extensive experiments based on synthetic and real data are conducted to investigate the efficacy and properties of the proposed Seq GAN. |
| Researcher Affiliation | Academia | Lantao Yu, Weinan Zhang, Jun Wang, Yong Yu Shanghai Jiao Tong University, University College London {yulantao,wnzhang,yyu}@apex.sjtu.edu.cn, j.wang@cs.ucl.ac.uk |
| Pseudocode | Yes | Algorithm 1 Sequence Generative Adversarial Nets |
| Open Source Code | Yes | Experiment code: https://github.com/Lantao Yu/Seq GAN |
| Open Datasets | Yes | For music composition, we use Nottingham7 dataset as our training data, which is a collection of 695 music of folk tunes in midi file format. ... 7http://www.iro.umontreal.ca/ lisa/deep/data |
| Dataset Splits | No | The paper mentions using a 'training set S' for synthetic data and 'training data' for real-world scenarios, and a 'test stage' for evaluation. However, it does not explicitly provide details about specific training, validation, and test dataset splits (e.g., percentages, sample counts, or explicit mention of a validation set). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions architectural components like LSTM and CNN, and optimization algorithms such as Adam and RMSprop, but it does not specify any software dependencies with version numbers (e.g., Python, TensorFlow, PyTorch, CUDA versions). |
| Experiment Setup | Yes | To set up the synthetic data experiments, we first initialize the parameters of an LSTM network following the normal distribution N(0, 1)... A curriculum rate ω is used to control the probability of replacing the true tokens with the generated ones. To get a good and stable performance, we decrease ω by 0.002 for every training epoch. ... the kernel size is from 1 to T and the number of each kernel size is between 100 to 2004. Dropout (Srivastava et al. 2014) and L2 regularization are used to avoid over-fitting. ... the g-steps, d-steps and k parameters in Algorithm 1 have a large effect on the convergence and performance of Seq GAN. Figure 3 shows the effect of these parameters. |