RankSim: Ranking Similarity Regularization for Deep Imbalanced Regression
Authors: Yu Gong, Greg Mori, Fred Tung
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We performed extensive experimental validation of Rank Sim on three public benchmarks for deep imbalanced regression. We first describe the benchmarks, metrics, and baselines. We then present experimental results on the three benchmarks, including comparisons with state-of-the-art approaches. Finally, we present ablation studies and analysis to better understand the impact of our design choices. |
| Researcher Affiliation | Collaboration | 1Simon Fraser University 2Borealis AI. |
| Pseudocode | No | The paper provides mathematical formulations and descriptions of operations, such as rk(a), but does not present them within a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | Code and pretrained weights are available at https://github.com/Borealis AI/ ranksim-imbalanced-regression. |
| Open Datasets | Yes | We consider three datasets recently introduced by Yang et al. (2021). IMDB-WIKI-DIR is an age estimation dataset derived from IMDB-WIKI (Rothe et al., 2016), which consists of face images with age annotations. |
| Dataset Splits | Yes | IMDB-WIKI-DIR is an age estimation dataset derived from IMDB-WIKI (Rothe et al., 2016), which consists of face images with age annotations. It has 191,509 training samples, 11,022 validation samples and 11,022 test samples; |
| Hardware Specification | Yes | We measured the training time for Age DB-DIR on four NVIDIA Ge Force GTX 1080 Ti GPUs. |
| Software Dependencies | No | The paper mentions software components such as 'Res Net-50', 'Bi LSTM', 'Glo Ve word embeddings', and 'Adam optimizer', but it does not specify their version numbers for reproducibility. |
| Experiment Setup | Yes | For all experiments, we use Res Net-50 as the backbone network. We use Adam optimizer with 0.9 momentum and 1e-4 weight decay. The learning rate is 1e-3 and the batch size is 256. We train all methods for 90 epochs with The learning rate scheduled to drop by 10 times at epoch 60 and epoch 80. The input image size is 224 by 224. For Rank Sim hyperparameters, we set the balancing weight γ as 100 and the interpolation strength λ as 2 for all experiments on IMDB-WIKI-DIR. |