Learning to Re-weight Examples with Optimal Transport for Imbalanced Classification
Authors: Dandan Guo, Zhuo Li, meixi zheng, He Zhao, Mingyuan Zhou, Hongyuan Zha
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on image, text and point cloud datasets demonstrate that our proposed re-weighting method has excellent performance, achieving state-of-the-art results in many cases and providing a promising tool for addressing the imbalanced classification issue. |
| Researcher Affiliation | Academia | Dandan Guo 1,2, Zhuo Li3,4, Meixi Zheng5, He Zhao 6, Mingyuan Zhou7, Hongyuan Zha1,8 1School of Data Science, The Chinese University of Hong Kong, Shenzhen 2 Institute of Robotics and Intelligent Manufacturing 3School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 4 Shenzhen Research Institute of Big Data 5Xidian University 6CSIRO s Data61 7The University of Texas at Austin 8Shenzhen Institute of Artificial Intelligence and Robotics for Society |
| Pseudocode | Yes | Algorithm 1 Workflow about our re-weighting method for optimizing θ and w. |
| Open Source Code | Yes | The code has been made available at https://github.com/Dandan Guo1993/reweight-imbalance-classification-with-OT. |
| Open Datasets | Yes | We evaluate our method on CIFAR-LT-10, CIFAR-LT-100, Image Net-LT and Places-LT. We create CIFAR-LT-10 (CIFAR-LT-100) from CIFAR-10 (CIFAR-100)[38] by downsampling samples per class with IF {200, 100, 50, 20} [5, 13]. Image Net-LT is built from the classic Image Net with 1000 classes[39] and IF=1280/5 [5, 24]. Places-LT is created from Places-2 [40] with 365 classes and IF=4980/5 [4, 24]. |
| Dataset Splits | Yes | Besides, consider a small balanced meta set Dmeta = {(xj, yj)}M j=1, where M is the amount of total samples and M N. ... We randomly select 10 training images per class as meta set [5]; see more details in Appendix B. |
| Hardware Specification | Yes | Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] See Appendix B. |
| Software Dependencies | No | The paper mentions software like 'ResNet-32' and 'BERT (base, uncased) model' but does not provide specific version numbers for any libraries or frameworks used in the implementation (e.g., PyTorch version, TensorFlow version). |
| Experiment Setup | Yes | For a fair comparison, we use Res Net-32 [41] as the backbone on CIFAR-LT-10 and CIFAR-LT-100. Following Li et al. [5], at stage 1, we use 200 epochs, set the learning rate α of θ as 0.1, which is decayed by 1e-2 at the 160th and 180th epochs. At stage 2, we use 40 epochs, set α as 2e-5 and learning rate β of weights as 1e-3. We use the SGD optimizer with momentum 0.9, weight decay 5e-4 and set the batch size as 16. |