Auto Seg-Loss: Searching Metric Surrogates for Semantic Segmentation
Authors: Hao Li, Chenxin Tao, Xizhou Zhu, Xiaogang Wang, Gao Huang, Jifeng Dai
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on PASCAL VOC and Cityscapes demonstrate that the searched surrogate losses outperform the manually designed loss functions consistently. |
| Researcher Affiliation | Collaboration | Hao Li1 , Chenxin Tao2 , Xizhou Zhu3, Xiaogang Wang1,3, Gao Huang2, Jifeng Dai3,4 1The Chinese University of Hong Kong 2Tsinghua University 3Sense Time Research 4Qing Yuan Research Institute, Shanghai Jiao Tong University |
| Pseudocode | Yes | Algorithm 1: Auto Seg-Loss Parameter Search |
| Open Source Code | Yes | Code shall be released at https://github.com/fundamentalvision/Auto-Seg-Loss. |
| Open Datasets | Yes | We evaluate on the PASCAL VOC 2012 (Everingham et al., 2015) and the Cityscapes (Cordts et al., 2016) datasets. |
| Dataset Splits | Yes | During the surrogate parameter search, we randomly sample 1500 training images in PASCAL VOC and 500 training images in Cityscapes to form the hold-out set Shold-out, respectively. The remaining training images form the training set Strain in search. |
| Hardware Specification | Yes | The search time is counted on 8 NVIDIA Tesla V100 GPUs. |
| Software Dependencies | No | The paper mentions deep learning models (e.g., Deeplabv3+, Res Net-50/101, PSPNet, HRNet), optimization algorithms (SGD), and a search algorithm (PPO2), but it does not specify version numbers for any of the underlying software frameworks or libraries (e.g., PyTorch, TensorFlow, scikit-learn versions). |
| Experiment Setup | Yes | During the surrogate parameter search, we randomly sample 1500 training images in PASCAL VOC and 500 training images in Cityscapes to form the hold-out set Shold-out, respectively. The remaining training images form the training set Strain in search. µ0 is set to make g(y; θ) = y. The backbone network is Res Net-50. The images are down-sampled to be of 128 128 resolution. SGD lasts only 1000 iterations with a mini-batch size of 32. |