Improving Generative Adversarial Networks via Adversarial Learning in Latent Space
Authors: Yang Li, Yichuan Mo, Liangliang Shi, Junchi Yan
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on visual data show that our method can effectively achieve improvement in both quality and diversity. The implementation is publicly available at https://github.com/yangco-le/Adv Lat GAN. |
| Researcher Affiliation | Academia | Department of Computer Science and Engineering, Mo E Key Lab of Artificial Intelligence, Shanghai Jiao Tong University 2Key Lab. of Machine Perception (Mo E), School of Intelligence Science and Technology, Peking University {yanglily,shiliangliang,yanjunchi}@sjtu.edu.cn mo666666@stu.pku.edu.cn Junchi Yan is the correspondence author who is also with Shanghai AI Laboratory. |
| Pseudocode | Yes | We present the algorithm in Alg. 1 in the appendix which we call Adversarial Latent GAN with a post meaning quality (Adv Lat GAN-qua)... we finally develop our new diversity enhancing GAN training approach Adv Lat GAN-div as shown in Alg. 2 in the appendix... |
| Open Source Code | Yes | The implementation is publicly available at https://github.com/yangco-le/Adv Lat GAN. |
| Open Datasets | Yes | Experiments on Image Net and Celeb A are performed on GPUs of Tesla V100. Other public benchmark results are performed on a single GPU of Ge Force RTX 3090. We test Adv Lat GAN-z on Grid and Ring datasets... Results on MNIST. Results on STL-10. Results on AFHQ and FFHQ. Table 4 shows the results on Cifar-10 and STL-10... Table. 5 presents results on larger datasets: LSUN Church [59], Celeb A [60] and Image Net [61]... |
| Dataset Splits | No | No explicit mention of a 'validation set' or its specific split percentage/count was found in the provided text. |
| Hardware Specification | Yes | Experiments on Image Net and Celeb A are performed on GPUs of Tesla V100. Other public benchmark results are performed on a single GPU of Ge Force RTX 3090. |
| Software Dependencies | No | No specific software dependencies with version numbers were explicitly mentioned. |
| Experiment Setup | Yes | The number of latent iterations in training is set as one per discriminator step... The iteration step size ϵ of Adv Lat GAN-z is set to 0.05 and we conduct 20 steps each time. The iteration step size ϵ of Adv Lat GAN-z is set to 0.01 for SNGAN and 0.002 for WGANGP and we conduct 100 steps each time. |