CL Clean

Contrastive Learning for Label Noise Recognition

Introduction

Contrastive learning has been pushing the benchmarks for representation learning without the use of (reliable) labels. This project will leverage the power of these approaches to automatically flag instances in time-series dataset that are the most likely to be mislabeled.

Sources

Contrastive Learning frameworks: https://github.com/tian0426/cl-har

4 real world datasets have been used in this work: -UniMiB SHAR -UCI HAR -TWristAR -Sussex-HuaWei Locomotion Data loaders for these datasets are provided in src/load_data_time_series

Use

To run the 4 provided experiments on one dataset use:

python3 main.py --set [dataset name]

The supported names are: -synthetic -unimib -uci har -sussex huawei