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[IJCAI'23] Stochastic Feature Averaging for Learning with Long-Tailed Noisy Labels

PyTorch Code for the following paper at IJCAI2023

<b>Title</b>: <i>Stochastic Feature Averaging for Learning with Long-Tailed Noisy Labels</i>

<b>Abstract</b> Deep neural networks have shown promising results on a wide variety of tasks using large-scale and well-annotated training datasets. However, data collected from real-world applications can suffer from two prevalent biases, i.e., long-tailed class distribution and label noise. Previous efforts on long-tailed learning and label-noise learning can only address a single type of data bias, leading to a severe deterioration of their performance. In this paper, we propose a distance-based sample selection algorithm called Stochastic Feature Averaging (SFA), which fits a Gaussian using the exponential running average of class centroids to capture uncertainty in representation space due to label noise and data scarcity. With SFA, we detect noisy samples based on their distances to class centroids sampled from this Gaussian distribution. Based on the identified clean samples, we then propose to train an auxiliary balanced classifier to improve the generalization for the minority class and facilitate the update of Gaussian parameters. Extensive experimental results show that SFA can enhance the performance of existing methods on both simulated and real-world datasets. Further, we propose to combine SFA with the sample-selection approach, distribution-robust, and noise-robust loss functions, resulting in significant improvement over the baselines.

<b>Illustration</b> <img src="./img/framework.png">

<b>Environment</b>

<b>Experiments</b>

First, install dependencies listed in requirements.txt:

pip install -r requirements.txt

Next, make a directory to store your dataset

mkdir path_to_your_data

Then, run the following commands to conduct the experiment:

# Experiment on CIFAR with given label noise and class imbalance
python Train_cifar.py --dataset cifar10 --noise_ratio 0.5 --imb_factor 0.01 --data_path path_to_your_data

# Experiment on WebVision dataset
python Train_webvision.py --data_path path_to_your_data

<b>Acknowledgement</b>
Thanks the following repositories for code reference:

RoLT,

Classifier-Balancing,

DivideMix, etc.

<b>Cite SFA</b>
If you find the code useful in your research, please consider citing our paper:

<pre> @inproceedings{ li2023sfa, title={Stochastic Feature Averaging for Learning with Long-Tailed Noisy Labels}, author={Hao-Tian Li and Tong Wei and Hao Yang and Kun Hu and Chong Peng and Li-Bo Sun and Xun-Liang Cai and Min-Ling Zhang}, booktitle={Proceedings of the International Joint Conference on Artificial Intelligence}, pages={3902--3910}, year={2023}, }</pre>

<b>License</b>
This project is licensed under the terms of the MIT license.