This repository is the official PyTorch implementation of WVN-RS, introduced in Adjusting Decision Boundary for Class Imbalanced Learning.
- NVIDIA docker : Docker image will be pulled from cloud.
- CIFAR dataset : The "dataset_path" in run_cifar.sh should be
cifar10/
data_batch_N
test_batch
cifar100/
train
test
CIFAR datasets are available here.
Run the shell script.
bash run_cifar.sh
To use Weight Vector Normalization (WVN), use --WVN flag. (It is already in the script.)
- Validation error on Long-Tailed CIFAR10
| Imbalance | 200 | 100 | 50 | 20 | 10 | 1 |
|---|---|---|---|---|---|---|
| Baseline | 35.67 | 29.71 | 22.91 | 16.04 | 13.26 | 6.83 |
| Over-sample | 32.19 | 28.27 | 21.40 | 15.23 | 12.24 | 6.61 |
| Focal | 34.71 | 29.62 | 23.28 | 16.77 | 13.19 | 6.60 |
| CB | 31.11 | 25.43 | 20.73 | 15.64 | 12.51 | 6.36 |
| LDAM-DRW | 28.09 | 22.97 | 17.83 | 14.53 | 11.84 | 6.32 |
| Baseline+RS | 27.02 | 21.36 | 17.16 | 13.46 | 11.86 | 6.32 |
| WVN+RS | 27.23 | 20.17 | 16.80 | 12.76 | 10.71 | 6.29 |
- Validation error on Long-Tailed CIFAR100
| Imbalance | 200 | 100 | 50 | 20 | 10 | 1 |
|---|---|---|---|---|---|---|
| Baseline | 64.21 | 60.38 | 55.09 | 48.93 | 43.52 | 29.69 |
| Over-sample | 66.39 | 61.53 | 56.65 | 49.03 | 43.38 | 29.41 |
| Focal | 64.38 | 61.31 | 55.68 | 48.05 | 44.22 | 28.52 |
| CB | 63.77 | 60.40 | 54.68 | 47.41 | 42.01 | 28.39 |
| LDAM-DRW | 61.73 | 57.96 | 52.54 | 47.14 | 41.29 | 28.85 |
| Baseline+RS | 59.59 | 55.65 | 51.91 | 45.09 | 41.45 | 29.80 |
| WVN+RS | 59.48 | 55.50 | 51.80 | 46.12 | 41.02 | 29.22 |
This codes use docker image "feidfoe/pytorch:v.2" with pytorch version, '0.4.0a0+0640816'. The image only provides basic libraries such as NumPy or PIL.
WVN is implemented on ResNet architecture only.
This repository is forked and modified from original repo.
Byungju Kim (byungju.kim@kaist.ac.kr)
@ARTICLE{9081988,
author={B. {Kim} and J. {Kim}},
journal={IEEE Access},
title={Adjusting Decision Boundary for Class Imbalanced Learning},
year={2020},
volume={8},
number={},
pages={81674-81685},}