Improving Deep Regression with Ordinal Entropy
Authors: Shihao Zhang, Linlin Yang, Michael Bi Mi, Xiaoxu Zheng, Angela Yao
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic and real-world regression tasks demonstrate the importance and benefits of increasing entropy for regression. |
| Researcher Affiliation | Collaboration | 1National University of Singapore, Singapore, 2Huawei International Pte Ltd, Singapore |
| Pseudocode | No | No pseudocode or algorithm block was found in the paper. |
| Open Source Code | Yes | Code can be found here: https://github.com/needylove/Ordinal Entropy |
| Open Datasets | Yes | NYU-Depth-v2 (Silberman et al., 2012) provides indoor images with the corresponding depth maps at a pixel resolution 640 480. |
| Dataset Splits | No | No explicit specification of training/validation/test splits with percentages or counts was provided. For real-world datasets, it mentions 'train/test split given used by previous works (Bhat et al., 2021; Yuan et al., 2022)' and for synthetic data, it only specifies '1k data as the training set and test on the testing set with 100k samples' without detailing a validation split. |
| Hardware Specification | No | No specific hardware details such as GPU models (e.g., NVIDIA A100), CPU models, or detailed computer specifications were provided for running the experiments. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., 'Python 3.8, PyTorch 1.9') were provided for replicating the experiment. |
| Experiment Setup | Yes | We use the trade-off parameters λd, λt the same value of 0.001, 1, 10, 1, for operator learning, depth estimation, crowd counting and age estimation, respectively. |