A systematic study of the class imbalance problem in convolutional neural networks
TLDR
The effect of class imbalance on classification performance is detrimental; the method of addressing class imbalance that emerged as dominant in almost all analyzed scenarios was oversampling; and thresholding should be applied to compensate for prior class probabilities when overall number of properly classified cases is of interest.About:
This article is published in Neural Networks.The article was published on 2018-10-01 and is currently open access. It has received 1777 citations till now. The article focuses on the topics: Deep learning & Convolutional neural network.read more
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A survey on Image Data Augmentation for Deep Learning
TL;DR: This survey will present existing methods for Data Augmentation, promising developments, and meta-level decisions for implementing DataAugmentation, a data-space solution to the problem of limited data.
Posted Content
CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
Pranav Rajpurkar,Jeremy Irvin,Kaylie Zhu,Brandon Yang,Hershel Mehta,Tony Duan,Daisy Yi Ding,Aarti Bagul,Curtis P. Langlotz,Katie Shpanskaya,Matthew P. Lungren,Andrew Y. Ng +11 more
TL;DR: An algorithm that can detect pneumonia from chest X-rays at a level exceeding practicing radiologists is developed, and it is found that CheXNet exceeds average radiologist performance on the F1 metric.
Proceedings ArticleDOI
Class-Balanced Loss Based on Effective Number of Samples
TL;DR: This work designs a re-weighting scheme that uses the effective number of samples for each class to re-balance the loss, thereby yielding a class-balanced loss and introduces a novel theoretical framework to measure data overlap by associating with each sample a small neighboring region rather than a single point.
Journal ArticleDOI
Survey on deep learning with class imbalance
TL;DR: Examination of existing deep learning techniques for addressing class imbalanced data finds that research in this area is very limited, that most existing work focuses on computer vision tasks with convolutional neural networks, and that the effects of big data are rarely considered.
Proceedings ArticleDOI
Learning a Unified Classifier Incrementally via Rebalancing
TL;DR: This work develops a new framework for incrementally learning a unified classifier, e.g. a classifier that treats both old and new classes uniformly, and incorporates three components, cosine normalization, less-forget constraint, and inter-class separation, to mitigate the adverse effects of the imbalance.
References
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TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
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ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
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Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Journal Article
Scikit-learn: Machine Learning in Python
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TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.