Learning Semantic-aware Normalization for Generative Adversarial Networks
Authors: Heliang Zheng, Jianlong Fu, Yanhong Zeng, Jiebo Luo, Zheng-Jun Zha
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that our approach outperforms the SOTA style-based approaches in both unconditional image generation and conditional image inpainting tasks. |
| Researcher Affiliation | Collaboration | Heliang Zheng1 , Jianlong Fu2, Yanhong Zeng3*, Jiebo Luo4, Zheng-Jun Zha1 1University of Science and Technology of China, Hefei, China 2Microsoft Research, Beijing, China 3Sun Yat-sen University, Guangzhou, China 4University of Rochester, Rochester, NY |
| Pseudocode | No | The paper describes algorithms and modules in text and equations, but does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | More details can be found in our code https://github.com/researchmm/Sari GAN. |
| Open Datasets | Yes | We conduct experiments on both unconditional image generation and conditional image inpainting. For unconditional image generation, we evaluate our Sari GAN on three datasets, including LSUN CATS [31], LSUN CARS [31], and FFHQ [2] dataset. For image inpainting, we use Paris Street View [32] for evaluation. |
| Dataset Splits | No | The paper mentions 'training images' and evaluates on datasets with standard splits, but does not explicitly provide specific percentages, sample counts, or detailed methodology for its own dataset splits (train/validation/test). |
| Hardware Specification | Yes | We use Py Torch [34] as our codebase and run each experiment on 8 Tesla V100 GPUs for 7 days. |
| Software Dependencies | No | The paper mentions using 'PyTorch' as the codebase, but does not provide a specific version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | The choice of hyperparameters: a smaller t in Equation 2 would better approximate one-hot vector, while also makes the optimizing harder since the gradient would vanish. We defer such a trade-off to the network by making t learnable, and the learned t is around 0.3. The setting of the threshold r (e.g.,0.1) in Equation 2 is conditioned on the group number g (e.g.,16), and the principle is that each channel would have c/g similar channels after activated on average. The λ1 and λ2 in Equation 7 is experimentally set to be 2 and 10, respectively. |