Reproduction of "Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images"

Installation

Use the provided requirements.txt to install required packages.

pip install -r requirements.txt

Run Multiple Results

To reproduce the tables in our paper, check the bash_scripts/ directory and run the appropriate script.

• datasets_and_ir_ratios.sh for Table 3, 10
• darbn_ablation.sh for Table 4
• augmentation.sh for Table 5
• balanced_cifar10.sh for Table 6
• resnet.sh for Table 7, 8
• compare_dataset.sh for Table 10
• search_inputnorm.sh for Table 14
• batch_size.sh for Table 15
• delta.sh for Table 16

Run Individual Experiments

You can also run individual experiments by directly calling train.py.

python train.py

By default, it uses the config values saved in default_cifar10lt.yaml. You can specify different default config files.

python train.py config_filepath=default_cifar10.yaml

You can also change individual config values through the command line.

python train.py batch_size=64

Generate Figures

To generate figures for analysis, refer to the notebooks in the root directory.

• analysis_tsne.ipynb for Figure 2, 8
• analysis_confusion_matrix.ipynb for Figure 3
• dataset_comparison.ipynb for Figure 4
• analysis_group_accuracy.ipynb for Figure 6
• analysis_histogram.ipynb for Figure 9, 10

Each notebook downloads a model from Google Drive using a link specified in storage.py unless it already exists in local filepath.