ChanLIM/Augmentation-for-LNL

[CVPR 2021] Code for "Augmentation Strategies for Learning with Noisy Labels".

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]

Abstract

Imperfect labels are ubiquitous in real-world datasets. Several recent successful methods for training deep neural networks (DNNs) robust to label noise have used two primary techniques: filtering samples based on loss during a warm-up phase to curate an initial set of cleanly labeled samples, and using the output of a network as a pseudo-label for subsequent loss calculations. In this paper, we evaluate different augmentation strategies for algorithms tackling the "learning with noisy labels" problem. We propose and examine multiple augmentation strategies and evaluate them using synthetic datasets based on CIFAR-10 and CIFAR-100, as well as on the real-world dataset Clothing1M. Due to several commonalities in these algorithms, we find that using one set of augmentations for loss modeling tasks and another set for learning is the most effective, improving results on the state-of-the-art and other previous methods. Furthermore, we find that applying augmentation during the warm-up period can negatively impact the loss convergence behavior of correctly versus incorrectly labeled samples. We introduce this augmentation strategy to the state-of-the-art technique and demonstrate that we can improve performance across all evaluated noise levels. In particular, we improve accuracy on the CIFAR-10 benchmark at 90% symmetric noise by more than 15% in absolute accuracy, and we also improve performance on the real-world dataset Clothing1M.

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

Benchmarks

All Benchmarks

Key

Annotation	Meaning
Small	Worse or equivalent to previous state-of-the-art
Normal	Better than previous state-of-the-art
Bold	Best in task/category

CIFAR-10

Model	Metric	Noise Type/Ratio
		20% sym	50% sym	80% sym	90% sym	40% asym
Runtime-W (Vanilla DivideMix)	Highest	96.100%	94.600%	93.200%	76.000%	93.400%
	Last 10	95.700%	94.400%	92.900%	75.400%	92.100%
Raw	Highest	85.940%			27.580%
	Last 10	83.230%			23.915%
Expansion.Weak	Highest	90.860%			31.220%
	Last 10	89.948%			10.000%
Expansion.Strong	Highest	90.560%			35.100%
	Last 10	89.514%			34.228%
AugDesc-WW	Highest	96.270%			36.050%
	Last 10	96.084%			23.503%
Runtime-S	Highest	96.540%			70.470%
	Last 10	96.327%			70.223%
AugDesc-SS	Highest	96.470%			81.770%
	Last 10	96.193%			81.540%
AugDesc-WS.RandAug.n1m6	Highest	96.280%			89.750%
	Last 10	96.006%			89.629%
AugDesc-WS.SAW	Highest	96.350%	95.640%	93.720%	35.330%	94.390%
	Last 10	96.138%	95.417%	93.563%	10.000%	94.078%
AugDesc-WS (WAW)	Highest	96.330%	95.360%	93.770%	91.880%	94.640%
	Last 10	96.168%	95.134%	93.641%	91.760%	94.258%

CIFAR-100

Model	Metric	Noise Type/Ratio
		20% sym	50% sym	80% sym	90% sym
Runtime-W (Vanilla DivideMix)	Highest	77.300%	74.600%	60.200%	31.500%
	Last 10	76.900%	74.200%	59.600%	31.000%
Raw	Highest	52.240%			7.990%
	Last 10	39.176%			2.979%
Expansion.Weak	Highest	57.110%			7.300%
	Last 10	53.288%			2.223%
Expansion.Strong	Highest	55.150%			7.540%
	Last 10	54.369%			3.242%
AugDesc-WW	Highest	78.900%			30.330%
	Last 10	78.437%			29.876%
Runtime-S	Highest	79.890%			40.520%
	Last 10	79.395%			40.343%
AugDesc-SS	Highest	79.790%			38.850%
	Last 10	79.511%			38.553%
AugDesc-WS.RandAug.n1m6	Highest	78.060%			36.890%
	Last 10	77.826%			36.672%
AugDesc-WS.SAW	Highest	79.610%	77.640%	61.830%	17.570%
	Last 10	79.464%	77.522%	61.632%	15.050%
AugDesc-WS (WAW)	Highest	79.500%	77.240%	66.360%	41.200%
	Last 10	79.216%	77.010%	66.046%	40.895%

Clothing1M

Model	Accuracy
Runtime-W (Vanilla DivideMix)	74.760%
AugDesc-WS (WAW)	74.720%
AugDesc-WS.SAW	75.109%

Summary Metrics

CIFAR-10

Model	Metric	Noise Type/Ratio
		20% sym	50% sym	80% sym	90% sym	40% asym
SOTA	Highest	96.100%	94.600%	93.200%	76.000%	93.400%
	Last 10	95.700%	94.400%	92.900%	75.400%	92.100%
Ours	Highest	96.540%	95.640%	93.770%	91.880%	94.640%
	Last 10	96.327%	95.417%	93.641%	91.760%	94.258%

CIFAR-100

Model	Metric	Noise Type/Ratio
		20% sym	50% sym	80% sym	90% sym
SOTA	Highest	77.300%	74.600%	60.200%	31.500%
	Last 10	76.900%	74.200%	59.600%	31.000%
Ours	Highest	79.890%	77.640%	66.360%	41.200%
	Last 10	79.511%	77.522%	66.046%	40.895%

Clothing1M

Model	Accuracy
SOTA	74.760%
Ours	75.109%

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 continutations 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 (temporary arXiv citation):

@ARTICLE{2021arXiv210302130N,
       author = {{Nishi}, Kento and {Ding}, Yi and {Rich}, Alex and {H{\"o}llerer}, Tobias},
        title = "{Augmentation Strategies for Learning with Noisy Labels}",
      journal = {arXiv e-prints},
     keywords = {Computer Science - Computer Vision and Pattern Recognition},
         year = 2021,
        month = mar,
          eid = {arXiv:2103.02130},
        pages = {arXiv:2103.02130},
archivePrefix = {arXiv},
       eprint = {2103.02130},
 primaryClass = {cs.CV},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2021arXiv210302130N},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

Additional Info

This repository is a fork of the official DivideMix implementation.