Augmentation-for-LNL

Code for Augmentation Strategies for Learning with Noisy Labels (CVPR 2021).

Authors: Kento Nishi*, Yi Ding*, Alex Rich, Tobias Höllerer [*: equal contribution]

View on arXiv / View PDF / Download Paper Source / Download Source Code

Benchmarks

Training Locally

The source code is heavily reliant on CUDA. Please make sure that you have the newest version of Pytorch and a compatible version of CUDA installed. Using older versions may exhibit inconsistent performance.

Download Pytorch / Download CUDA

Other requirements are included in requirements.txt.

Reproducibility

At particularly high noise ratios (ex. 90% on CIFAR-10), results may vary across training runs. We are aware of this issue, and are exploring ways to yield more consistent results. We will publish any findings (consistently performant configurations, improved procedures, etc.) both in this repository and in continuations of this work.

All training configurations and parameters are controlled via the presets.json file. Configurations can contain infinite subconfigurations, and settings specified in subconfigurations always override the parent.

To train locally, first add your local machine to the presets.json:

{
    // ... inside the root scope
    "machines": { // list of machines
        "localPC": { // name for your local PC, can be anything
            "checkpoint_path": "./localPC_checkpoints"
        }
    },
    "configs": {
        "c10": { // cifar-10 dataset
            "machines": { // list of machines
                "localPC": { // local PC name
                    "data_path": "/path/to/your/dataset"
                    // path to dataset (python) downloaded from:
                    // https://www.cs.toronto.edu/~kriz/cifar.html
                }
                // ... keep all other machines unchanged
            }
            // ... keep all other config values unchanged
        }
        // ... keep all other configs unchanged
    }
    // ... keep all other global values unchanged
}

A "preset" is a specific configuration branch. For example, if you would like to run train_cifar.py with the preset root -> c100 -> 90sym -> AugDesc-WS on your machine named localPC, you can run the following command:

python train_cifar.py --preset c100.90sym.AugDesc-WS --machine localPC

The script will begin training the preset specified by the --preset argument. Progress will be saved in the appropriate directory in your specified checkpoint_path. Additionally, if the --machine flag is ommitted, the training script will look for the dataset in the data_path inherited from parent configurations.

Here are some abbreviations used in our presets.json:

Abbreviation	Meaning
`c10`	CIFAR-10
`c100`	CIFAR-100
`c1m`	Clothing1M
`sym`	Symmetric Noise
`asym`	Asymmetric Noise
`SAW`	Strongly Augmented Warmup
`WAW`	Weakly Augmented Warmup
`RandAug`	RandAugment

Citations

Please cite the following:

@InProceedings{Nishi_2021_CVPR,
    author    = {Nishi, Kento and Ding, Yi and Rich, Alex and {H{\"o}llerer, Tobias},
    title     = {Augmentation Strategies for Learning With Noisy Labels},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {8022-8031}
}