Discover, Hallucinate, and Adapt: Open Compound Domain Adaptation for Semantic Segmentation
Authors: KwanYong Park, Sanghyun Woo, Inkyu Shin, In So Kweon
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our solution on standard benchmark GTA5 to C-driving, and achieved new state-of-the-art results. To empirically verify the efficacy of our proposals, we conduct extensive ablation studies. |
| Researcher Affiliation | Academia | Kwanyong Park, Sanghyun Woo, Inkyu Shin, In So Kweon Korea Advanced Institute of Science and Technology (KAIST) {pkyong7,shwoo93,dlsrbgg33,iskweon77}@kaist.ac.kr |
| Pseudocode | No | The paper describes its proposed algorithm verbally and with diagrams (Figure 1, Figure 2) but does not provide pseudocode or a formal algorithm block. |
| Open Source Code | No | The paper does not provide any explicit links or statements regarding the availability of its source code. |
| Open Datasets | Yes | In our adaptation experiments, we take GTA5 [33] as the source domain, while the BDD100K dataset [41] is adopted as the compound ( rainy , snowy , and cloudy ) and open domains ( overcast ) (i.e., C-Driving [23]). |
| Dataset Splits | No | The paper mentions training schemes like "For the short training scheme (5K iteration)" and "For the longer training scheme (150K iteration)" but does not specify dataset splits (e.g., training, validation, test percentages or sample counts) needed for reproduction. |
| Hardware Specification | No | The paper uses a pre-trained VGG-16 [36] as backbone network but does not specify the hardware (e.g., GPU model, CPU type) used for training or inference. |
| Software Dependencies | No | The paper mentions using LS GAN [27] for Adapt-step training and an ImageNet-pretrained VGG model, but it does not specify version numbers for any software, libraries, or frameworks (e.g., Python, PyTorch, TensorFlow, CUDA versions). |
| Experiment Setup | Yes | Here, we use λGAN = 1, λsem = 10, λStyle = 10, λOut = 0.01, λtask = 1. For the short training scheme (5K iteration), we follow the same experimental setup of [23]. For the longer training scheme (150K iteration), we use LS GAN [27] for Adapt-step training. |