TL;DR

CILIATE is a Class-based Incremental Learning (CIL) model automatic repairing tool, which can help identify important samples and train the model using the debiased training method on these samples to fix model fairness bugs. It does not require modifying model training protocol or architecture.

Repo Structure

1. data: It contains three datasets we used in this paper.

2. main.py: It contains the source codes of this work and a demo. The way to run the demo cases has been shown here.

3. misc: The README.md shows how to use our demos, the repo structure, the way to reproduce our experiments and our experiment results. And the requirement.txt shows all the dependencies of this work.

- data/
- cars.py
- cifar100.py
- dataset.py
- flowers.py
- imbalanced.py
- main.py
- model.py
- README.md
- requirements.txt
- trainer.py

Setup

(Recommended) Create a virtual environment

CILIATE requires specific versions of some Python packages which may conflict with other projects on your system. A virtual environment is strongly recommended to ensure the requirements may be installed safely.

Install with pip

To install the requirements, run:

pip install -r ./requirements.txt

Note: The version of Pytorch in this requirements is CPU version. If you need GPU version, please check your CUDA version and install Pytorch manually.

Run demo

for cifar-100 dataset:

python main.py 

for flowers dataset:

python main.py --dataset flowers --total_cls 102

for cars dataset:

python main.py --dataset cars --total_cls 196

The optional Args are:

Argument	Help	Default
--dataset	(Char) Choose dataset. Include "cifar100", "flowers" and "cars"	cifar100
--epoch	(Int) Training epoch for network	250
--batch-size	(Int) Batch size of dataloader	32
--lr	(Float) Learning rate	0.1
--ita	(Float) Hyperparameter $\eta$ of CILIATE	0.1
--total_cls	(Int) Total classes of dataset	100