The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition

Krause, Jonathan; Sapp, Benjamin; Howard, Andrew; Zhou, Howard; Toshev, Alexander; Duerig, Tom; Philbin, James; Li, Feifei

doi:10.1007/978-3-319-46487-9_19

Cited by 282 publications

(271 citation statements)

References 59 publications

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“…Text-mining module: When this was not used, the overall accuracy dropped as the irrelevant training labels brought noises. However, the performance did not degrade substantially, showing that our model is able to tolerate noisy labels to a certain degree [18]. We also found training with the relevant + uncertain labels was better than using relevant labels only, which is because most uncertain labels are radiologists' inferences that are very likely to be true, especially if we only consider the lesion's appearance.…”

Section: Methodsmentioning

confidence: 78%

“…Noisy and incomplete training labels often exist in datasets mined from the web [7], which is similar to our labels mined from reports. Strategies to handle them include data filtering [18], noise-robust losses [31], noise modeling [25], finding reliable negative samples [20], and so on. We use a text-mining module to filter noisy positive labels and leverage label relation to find reliable negative labels.…”

Section: Related Workmentioning

confidence: 99%

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Holistic and Comprehensive Annotation of Clinically Significant Findings on Diverse CT Images: Learning From Radiology Reports and Label Ontology

Yan

Peng²,

Sandfort³

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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In radiologists' routine work, one major task is to read a medical image, e.g., a CT scan, find significant lesions, and describe them in the radiology report. In this paper, we study the lesion description or annotation problem. Given a lesion image, our aim is to predict a comprehensive set of relevant labels, such as the lesion's body part, type, and attributes, which may assist downstream fine-grained diagnosis. To address this task, we first design a deep learning module to extract relevant semantic labels from the radiology reports associated with the lesion images. With the images and text-mined labels, we propose a lesion annotation network (LesaNet) based on a multilabel convolutional neural network (CNN) to learn all labels holistically. Hierarchical relations and mutually exclusive relations between the labels are leveraged to improve the label prediction accuracy. The relations are utilized in a label expansion strategy and a relational hard example mining algorithm. We also attach a simple score propagation layer on LesaNet to enhance recall and explore implicit relation between labels. Multilabel metric learning is combined with classification to enable interpretable prediction. We evaluated LesaNet on the public DeepLesion dataset, which contains over 32K diverse lesion images. Experiments show that LesaNet can precisely annotate the lesions using an ontology of 171 finegrained labels with an average AUC of 0.9344. The labels of DeepLesion and the code have been released 1 . Lesion patch LesaNetNodule: Right mid lung: Lung mass: Perihilar: … Predict 0.93 0.92 0.89 0.64

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Section: Methodsmentioning

confidence: 78%

Section: Related Workmentioning

confidence: 99%

Holistic and Comprehensive Annotation of Clinically Significant Findings on Diverse CT Images: Learning From Radiology Reports and Label Ontology

Yan

Peng²,

Sandfort³

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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“…Besides visual information, researchers have been using additional information such as pose [5], attributes [25] and text description [17]. Data augmentation and transfer learning have also been studied [14,7].…”

Section: Related Workmentioning

confidence: 99%

Geo-Aware Networks for Fine-Grained Recognition

Chu

Potetz

Song

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

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Fine-grained recognition distinguishes among categories with subtle visual differences. In order to differentiate between these challenging visual categories, it is helpful to leverage additional information. Geolocation is a rich source of additional information that can be used to improve fine-grained classification accuracy, but has been understudied. Our contributions to this field are twofold. First, to the best of our knowledge, this is the first paper which systematically examined various ways of incorporating geolocation information into fine-grained image classification through the use of geolocation priors, post-processing or feature modulation. Secondly, to overcome the situation where no fine-grained dataset has complete geolocation information, we release 1 two fine-grained datasets with geolocation by providing complementary information to existing popular datasets -iNaturalist and YFCC100M. By leveraging geolocation information we improve top-1 accuracy in iNaturalist from 70.1% to 79.0% for a strong baseline image-only model. Comparing several models, we found that best performance was achieved by a post-processing model that consumed the output of the image-only baseline alongside geolocation. However, for a resource-constrained model (MobileNetV2), performance was better with a feature modulation model that trains jointly over pixels and geolocation: accuracy increased from 59.6% to 72.2%. Our work makes a strong case for incorporating geolocation information in fine-grained recognition models for both server and on-device.

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“…Countering the view that noise needs to be carefully modelled, [17] shows that a large quantity of noisy data can be effectively used to increase performance on a fine-grained classification task. From a similar perspective, [18] attempts to find the limits of weakly supervised data by pre-training with 3.5 billion images for classification and object detection tasks.…”

Section: Related Workmentioning

confidence: 99%

Training Object Detectors With Noisy Data

Chadwick

Newman

2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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The availability of a large quantity of labelled training data is crucial for the training of modern object detectors. Hand labelling training data is time consuming and expensive while automatic labelling methods inevitably add unwanted noise to the labels. We examine the effect of different types of label noise on the performance of an object detector. We then show how co-teaching, a method developed for handling noisy labels and previously demonstrated on a classification problem, can be improved to mitigate the effects of label noise in an object detection setting. We illustrate our results using simulated noise on the KITTI dataset and on a vehicle detection task using automatically labelled data.

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The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition

Cited by 282 publications

References 59 publications

Holistic and Comprehensive Annotation of Clinically Significant Findings on Diverse CT Images: Learning From Radiology Reports and Label Ontology

Holistic and Comprehensive Annotation of Clinically Significant Findings on Diverse CT Images: Learning From Radiology Reports and Label Ontology

Geo-Aware Networks for Fine-Grained Recognition

Training Object Detectors With Noisy Data

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