Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Label Encoding for Regression Networks
Authors: Deval Shah, Zi Yu Xue, Tor Aamodt
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate BEL on four complex regression problems: head pose estimation, facial landmark detection, age estimation, and end-to-end autonomous driving. |
| Researcher Affiliation | Academia | Deval Shah, Zi Yu Xue & Tor M. Aamodt Department of Electrical and Computer Engineering University of British Columbia, Vancouver, BC, Canada |
| Pseudocode | No | The paper describes methods through text and figures (e.g., Figure 1 for training/inference flow), but does not contain a dedicated pseudocode block or algorithm listing. |
| Open Source Code | Yes | Code is available at https://github.com/ubc-aamodt-group/BEL_regression. We have provided the training and inference code with trained models. |
| Open Datasets | Yes | We follow the evaluation setting of Hopenet (Ruiz et al., 2018) and FSA-Net (fsa, 2019) and use two evaluation protocols with three widely used datasets: 300W-LP (Zhu et al., 2016), BIWI (Fanelli et al., 2013), and AFLW2000 (Zhu et al., 2016). |
| Dataset Splits | Yes | In these experiments 20% of the training set is used as validation set and the validation error is used to choose the best BEL approach. |
| Hardware Specification | Yes | All experiments are conducted on a Linux machine with an Intel i9-9900X processor and an Nvidia RTX 2080 Ti GPU with 11GB of memory. |
| Software Dependencies | Yes | Our code is implemented using Python 3.8.3 with Pytorch 1.5.1 using CUDA 10.2. |
| Experiment Setup | Yes | We use two runs with different random seeds for each combination of learning rate {0.001, 0.0001, 0.00001} and batch size {8, 16} are used for hyperparameter tuning. |