Open AccessProceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TLDR
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.Abstract:
In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) 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. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.read more
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Salient Object Detection in the Deep Learning Era: An In-Depth Survey
TL;DR: This paper reviews deep SOD algorithms from different perspectives, including network architecture, level of supervision, learning paradigm, and object-/instance-level detection, and looks into the generalization and difficulty of existing SOD datasets.
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TabNet: Attentive Interpretable Tabular Learning
Sercan O. Arik,Tomas Pfister +1 more
TL;DR: It is demonstrated that TabNet outperforms other neural network and decision tree variants on a wide range of non-performance-saturated tabular datasets and yields interpretable feature attributions plus insights into the global model behavior.
Book ChapterDOI
Layer-Wise Relevance Propagation: An Overview
Grégoire Montavon,Alexander Binder,Sebastian Lapuschkin,Wojciech Samek,Klaus-Robert Müller,Klaus-Robert Müller,Klaus-Robert Müller +6 more
TL;DR: This chapter gives a concise introduction to LRP with a discussion of how to implement propagation rules easily and efficiently, how the propagation procedure can be theoretically justified as a ‘deep Taylor decomposition’, how to choose the propagation rules at each layer to deliver high explanation quality, and how LRP can be extended to handle a variety of machine learning scenarios beyond deep neural networks.
Book ChapterDOI
Out of Time: Automated Lip Sync in the Wild
Joon Son Chung,Andrew Zisserman +1 more
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A Theoretical Analysis of Contrastive Unsupervised Representation Learning
TL;DR: In contrastive learning as mentioned in this paper, the inner product of representations of similar pairs with each other is used to learn feature representations that are broadly useful in downstream classification tasks, and a theoretical framework for analyzing them is presented.
References
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Proceedings Article
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.
Proceedings ArticleDOI
ImageNet: A large-scale hierarchical image database
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
Proceedings ArticleDOI
Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).