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Title: Synthetic vs Real: Deep Learning on Controlled Noise

Paper content:
Performing controlled experiments on noisy data is essential in thoroughly understanding deep learning across a spectrum of noise levels.
Due to the lack of suitable datasets, previous research have only examined deep learning on controlled synthetic noise, and real-world noise has never been systematically studied in a controlled setting.
To this end, this paper establishes a benchmark of real-world noisy labels at 10 controlled noise levels.
As real-world noise possesses unique properties, to understand the difference, we conduct a large-scale study across a variety of noise levels and types, architectures, methods, and training settings.
Our study shows that: (1) Deep Neural Networks (DNNs) generalize much better on real-world noise.
(2) DNNs may not learn patterns first on real-world noisy data.
(3) When networks are fine-tuned, ImageNet architectures generalize well on noisy data.
(4) Real-world noise appears to be less harmful, yet it is more difficult for robust DNN methods to improve.
(5) Robust learning methods that work well on synthetic noise may not work as well on real-world noise, and vice versa.
We hope our benchmark, as well as our findings, will facilitate deep learning research on noisy data.
Deep Neural Networks (DNNs) trained on noisy data demonstrate intriguing properties.
For example, DNNs are capable of memorizing completely random training labels but generalize poorly on clean test data (Zhang et al., 2017) .
When trained with stochastic gradient descent, DNNs learn patterns first before memorizing the label noise (Arpit et al., 2017) .
These findings inspired recent research on noisy data.
As training data are usually noisy, the fact that DNNs are able to memorize the noisy labels highlights the importance of deep learning research on noisy data.
To study DNNs on noisy data, previous work often performs controlled experiments by injecting a series of synthetic noises into a well-annotated dataset.
The noise level p may vary in the range of 0%-100%, where p = 0% is the clean dataset whereas p = 100% represents the dataset of zero correct labels.
The most commonly used noise in the literature is uniform (or symmetric) labelflipping noise, in which the label of each example is independently and uniformly changed to a random (incorrect) class with probability p.
Controlled experiments on noise levels are essential in thoroughly understanding a DNN's properties across a spectrum of noise levels and faithfully comparing the strengths and weaknesses of different methods.
The synthetic noise enables researchers to experiment on controlled noise levels, and drives the development of theory and methodology in this field.
On the other hand, some studies were also verified on real-world noisy datasets, e.g. on WebVision (Li et al., 2017a) , Clothing-1M (Xiao et al., 2015) , Fine-grained Images (Krause et al., 2016) , and Instagram hashtags (Mahajan et al., 2018) , where the images are automatically tagged with noisy labels according to their surrounding texts.
However, these datasets do not provide true labels for the training images.
Their underlying noise levels are not only fixed but also unknown, rendering them infeasible for controlled studies on noise levels.
In this paper, we refer image-search noise in these datasets as "real-world noise" to distinguish it from synthetic label-flipping noise.
To study real-world noise in a controlled setting, we establish a benchmark of controlled real-world noisy labels, building on two existing datasets for coarse and fine-grained image classification: MiniImageNet (Vinyals et al., 2016) and Stanford Cars (Krause et al., 2013) .
We collect noisy labels using text-to-image and image-to-image search via Google Image Search.
Every training image is independently annotated by 3-5 workers, resulting in a total of 527,489 annotations over 147,108 images.
We create ten different noise levels from 0% to 80% by gradually replacing the original images with our annotated noisy images.
Our new benchmark will enable future research on the real-world noisy data with a controllable noise level.
We find that real-world noise possesses unique properties in its visual/semantic relevance and underlying class distribution.
To understand the differences, we conduct a large-scale study comparing synthetic noise, namely blue-pilled noise (or Blue noise), and real-world noise (or Red noise 1 ).
Specifically, we train DNNs across 10 noise levels, 7 network architectures, 6 existing robust learning methods, and 2 training settings (fine-tuning and training from random initialization).
Our study reveals several interesting findings.
First, we find that DNNs generalize much better on real-world noise than synthetic noise.
Our results verify Zhang et al. (2017) 's finding of deep learning generalization on synthetic noise.
However, we observe a considerably smaller generalization gap on real-world noise.
This does not mean that real-world noise is easier to tackle.
On the contrary, we find that real-world noise is more difficult for robust DNNs to improve.
Second, our results substantiate Arpit et al. (2017) 's finding that DNNs learn patterns first on noisy data.
But we find this behavior becomes insignificant on real-world noise and completely disappears on the fine-grained classification dataset.
This finding lets us rethink the role of "early stopping" (Yao et al., 2007; Arpit et al., 2017) on real-world noisy data.
Third, we find that when networks are fine-tuned, ImageNet architectures generalize well on noisy data, with a correlation of r = 0.87 and 0.89 for synthetic and real-world noise, respectively.
This finding generalizes Kornblith et al. (2019) 's finding, i.e. ImageNet architectures generalize well across clean datasets, to the noisy data.
Our contribution is twofold.
First, we establish a large benchmark of controlled real image search noise.
Second, we conduct perhaps the largest study in the literature to understand DNN training across a wide variety of noise levels and types, architectures, methods, and training settings.
We hope our benchmark along with our findings, resulted from a considerable amount of manual labeling effort (∼520K annotations) and computing resources (∼3K experiments), will facilitate future deep learning research on real-world noisy data.
Our main findings are summarized as follows:
1. DNNs generalize much better on real-world noise than synthetic noise.
Real-world noise appears to be less harmful, yet it is more difficult for robust DNN methods to improve.
2. DNNs may not learn patterns first on the real-world noisy data.
3. When networks are fine-tuned, ImageNet architectures generalize well on noisy data.
4. Adding noisy examples to a clean dataset may improve performance as long as the noise level is below a certain threshold (30% in our experiments).
In this paper, we established a benchmark for controlled real-world noise.
On the benchmark, we conducted a large-scale study to understand deep learning on noisy data across a variety of settings.
Our studies revealed a number of new findings, improving our understanding of deep learning on noisy data.
By comparing six robust deep learning methods, we found that real-world noise is more difficult to improve and methods that work well on synthetic noise may not work as well on realworld noise, and vice versa.
This encourages future research to be also carried out on controlled real-world noise.
We hope our benchmark, as well as our findings, will facilitate deep learning research on real-world noisy data.

assistant: text: This paper compares 6 existing noisy label learning methods in two training settings: from scratch, and finetuning.