Learning to Annotate Part Segmentation with Gradient Matching
Authors: Yu Yang, Xiaotian Cheng, Hakan Bilen, Xiangyang Ji
ICLR 2022 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our method is evaluated with semi-supervised part segmentation tasks and significantly outperforms other semi-supervised competitors when the amount of labelled examples is extremely limited. |
| Researcher Affiliation | Academia | Yu Yang Department of Automation Tsinghua University, BNRist EMAIL Xiaotian Cheng Department of Automation Tsinghua University, BNRist EMAIL Hakan Bilen School of Informatics University of Edinburgh EMAIL Xiangyang Ji Department of Automation Tsinghua University, BNRist EMAIL |
| Pseudocode | Yes | Algorithm 1: Learning to annotate with gradient matching. Algorithm 2: Learning to annotate with K-step MAML. |
| Open Source Code | Yes | Code is available at https://github.com/yangyu12/lagm. |
| Open Datasets | Yes | We evaluate our method on six part segmentation datasets: Celeb A, Pascal-Horse, Pascal Aeroplane, Car-20, Cat-16, and Face-34. Celeb A (Liu et al., 2015)... Pascal Part (Chen et al., 2014)... LSUN (Yu et al., 2015)... Car-20, Cat-16, and Face-34, released by Zhang et al. (2021)... |
| Dataset Splits | Yes | We finally obtain 180 training images, 34 validation images, and 225 test images to constitute Pascal-Horse, 180 training images, 78 validation images, and 269 test images to constitute Pascal-Aeroplane. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) are mentioned in the paper, only general terms like 'GPU memory'. |
| Software Dependencies | No | The paper mentions software like PyTorch, Deep Labv3, and U-Net, and links to a GitHub repository for Style GAN models, but does not provide specific version numbers for these software dependencies (e.g., 'PyTorch library' without a version). |
| Experiment Setup | Yes | The annotator and the segmentation network are optimized with an SGD optimizer with learning rate 0.001 and momentum 0.9. By default, we jointly train an annotator and a segmentation network with K = 1 and batch size 2 for 150,000 steps. |