Improving Gradient Flow with Unrolled Highway Expectation Maximization
Authors: Chonghyuk Song, Eunseok Kim, Inwook Shim9704-9712
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We achieve significant improvement in performance on several semantic segmentation benchmarks and empirically show that HEMNet effectively alleviates gradient decay. |
| Researcher Affiliation | Industry | Ground Technology Research Institute, Agency for Defense Development {chonghyuk.song, a18700, iwshim}@add.re.kr |
| Pseudocode | No | The paper describes algorithmic steps using equations (e.g., E-step and M-step equations), but it does not present a structured pseudocode block or a clearly labeled algorithm. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | Yes | We use Res Net (He et al. 2016a) pretrained on Image Net (Russakovsky et al. 2015) with multi-grid (Chen et al. 2017) as the backbone network. We set the step size to η = 0.5 for PASCAL VOC (Everingham et al. 2010) and PASCAL Context (Mottaghi et al. 2014), and η = 0.6 for COCO Stuff (Caesar, Uijlings, and Ferrari 2018). |
| Dataset Splits | No | The paper mentions evaluating on the PASCAL VOC val set, but does not specify the train/validation/test splits (e.g., percentages or counts) used for their experiments. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU models, CPU types, or memory used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies (e.g., Python, PyTorch, TensorFlow, specific libraries). |
| Experiment Setup | Yes | We use Res Net-50 with an output stride (OS) of 16 for all ablation studies and Resnet-101 with OS = 8 for comparisons with other state-of-the-art approaches. We set the temperature σ2 = C following (Vaswani et al. 2017), where C is the number of input channels to HEMNet, which is set to 512 by default. The step size is set to η = 0.5 for PASCAL VOC (Everingham et al. 2010) and PASCAL Context (Mottaghi et al. 2014), and η = 0.6 for COCO Stuff (Caesar, Uijlings, and Ferrari 2018). We set the training iteration number to Ttrain = 3 for all datasets. We use the moving average mechanism (Ioffe and Szegedy 2015; Li et al. 2019) to update the initial Gaussian bases µ(0). We adopt the mean Intersection-over-Union (m Io U) as the performance metric for all experiments across all datasets. |