Improving Deep Regression with Tightness
Authors: Shihao Zhang, Yuguang Yan, Angela Yao
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | 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. |