Delving into Deep Imbalanced Regression
Authors: Yuzhe Yang, Kaiwen Zha, Yingcong Chen, Hao Wang, Dina Katabi
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We curate and benchmark large-scale DIR datasets from common real-world tasks in computer vision, natural language processing, and healthcare domains. Extensive experiments verify the superior performance of our strategies. |
| Researcher Affiliation | Academia | 1MIT Computer Science & Artificial Intelligence Laboratory 2Department of Computer Science, Rutgers University. |
| Pseudocode | No | The paper describes its methods (LDS and FDS) in prose and mathematical equations but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and data are available at: https://github.com/ Yyz Harry/imbalanced-regression. |
| Open Datasets | Yes | We curate five DIR benchmarks that span computer vision, natural language processing, and healthcare. Fig. 6 shows the label density distribution of these datasets, and their level of imbalance. IMDB-WIKI-DIR (age): We construct IMDB-WIKI-DIR using the IMDB-WIKI dataset (Rothe et al., 2018)... Age DB-DIR (age): Age DB-DIR is constructed in a similar manner from the Age DB dataset (Moschoglou et al., 2017)... STS-B-DIR (text similarity score): We construct STS-BDIR from the Semantic Textual Similarity Benchmark (Cer et al., 2017; Wang et al., 2018)... NYUD2-DIR (depth): We create NYUD2-DIR based on the NYU Depth Dataset V2 (Nathan Silberman & Fergus, 2012)... SHHS-DIR (health condition score): We create SHHSDIR based on the SHHS dataset (Quan et al., 1997)... |
| Dataset Splits | Yes | IMDB-WIKI-DIR (age): We construct IMDB-WIKI-DIR using the IMDB-WIKI dataset (Rothe et al., 2018), which contains 523.0K face images and the corresponding ages. We filter out unqualified images, and manually construct balanced validation and test set over the supported ages... Overall, the curated dataset has 191.5K images for training, 11.0K images for validation and testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU models, CPU types) used for running experiments. |
| Software Dependencies | No | The paper mentions various models and techniques (e.g., Res Net-50, Bi LSTM, GloVe, Focal-R) but does not list any specific software dependencies with version numbers (e.g., PyTorch 1.9, TensorFlow 2.x). |
| Experiment Setup | No | The paper states, 'All training details, hyper-parameter settings, and additional results are provided in Appendix C and D,' indicating that these details are not in the main text. |