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..
Improving Deep Regression with Tightness
Authors: Shihao Zhang, Yuguang Yan, Angela Yao
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experiment on three deep regression tasks: age estimation, depth estimation, and coordinate prediction and compare with Rank Sim (Gong et al., 2022), Ordinal Entropy (OE) (Zhang et al., 2023), and PH-Reg (Zhang et al., 2024). ... Tables 1 and 2 show results on age estimation and depth estimation respectively. ... We conduct the ablation study on Age DB-DIR for age estimations. The results are given in Table 5. |
| Researcher Affiliation | Academia | Shihao Zhang1, Yuguang Yan2, Angela Yao1 1National University of Singapore 2Guangdong University of Technology EMAIL EMAIL EMAIL |
| Pseudocode | No | The paper describes methods and theoretical analysis in prose, but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code: https://github.com/needylove/Regression_tightness. |
| Open Datasets | Yes | For age estimation, we use Age DB-DIR (Yang et al., 2021)... For depth estimation, we use NYUD2-DIR (Yang et al., 2021)... |
| Dataset Splits | Yes | Both Age DB-DIR and NYUD2-DIR contain three disjoint subsets (i.e., Many, Med, and Few) divided from the whole set. |
| Hardware Specification | No | The paper mentions training times and memory consumption in Table 6, but does not provide specific hardware details such as GPU or CPU models used for the experiments. |
| Software Dependencies | No | The paper does not explicitly state specific software dependencies with version numbers, such as programming languages, libraries, or frameworks (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | For age estimation, we use Age DB-DIR... γ and λ are set to 0.1 and 100, respectively. We set the total target dimension M to be 8 for both tasks. For depth estimation, we use NYUD2-DIR... γ and λ are set to 0.05 and 10, respectively. We set the total target dimension M to be 8 for both tasks. ... We monitor the time and memory consumption for training a model from the beginning to the end with a batch size equal to 128. |