Deep Residual Learning for Image Recognition
Kaiming He,Xiangyu Zhang,Shaoqing Ren,Jian Sun +3 more
- pp 770-778
TLDR
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.Abstract:
Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8× deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.read more
Citations
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Journal ArticleDOI
Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge.
Arnaud Arindra Adiyoso Setio,Alberto Traverso,Thomas de Bel,Moira S.N. Berens,Cas van den Bogaard,Piergiorgio Cerello,Hao Chen,Qi Dou,Maria Evelina Fantacci,Bram Geurts,Robbert van der Gugten,Pheng-Ann Heng,Bart Jansen,Michael M.J. de Kaste,Valentin Kotov,Jack Yu-Hung Lin,Jeroen Manders,Alexander Sóñora-Mengana,Juan C. García-Naranjo,Evgenia Papavasileiou,Mathias Prokop,M. Saletta,Cornelia M. Schaefer-Prokop,Ernst T. Scholten,Luuk Scholten,Miranda M. Snoeren,Ernesto Lopez Torres,Jef Vandemeulebroucke,Nicole Walasek,Guido Zuidhof,Bram van Ginneken,Colin Jacobs +31 more
TL;DR: The LUNA16 challenge is described, an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC‐IDRI data set, and the results so far are presented.
Journal ArticleDOI
AutoML: A survey of the state-of-the-art
Xin He,Kaiyong Zhao,Xiaowen Chu +2 more
TL;DR: A comprehensive and up-to-date review of the state-of-the-art (SOTA) in AutoML methods according to the pipeline, covering data preparation, feature engineering, hyperparameter optimization, and neural architecture search (NAS).
Book ChapterDOI
Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification
TL;DR: In this article, it was shown that it is possible to replace many of the expensive 3D convolutions by low-cost 2D convolution, and the best result was achieved when replacing the 3D CNNs at the bottom of the network, suggesting that temporal representation learning on high-level semantic features is more useful.
Posted Content
Meta-Learning with Latent Embedding Optimization
Andrei Rusu,Dushyant Rao,Jakub Sygnowski,Oriol Vinyals,Razvan Pascanu,Simon Osindero,Raia Hadsell +6 more
TL;DR: In this article, a data-dependent latent generative representation of model parameters is learned and a gradient-based meta-learning is performed in a low-dimensional latent space for few-shot learning.
Journal ArticleDOI
A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load
TL;DR: An end-to-end method that takes raw temporal signals as inputs and thus doesn’t need any time consuming denoising preprocessing and can achieve high accuracy when working load is changed is proposed.
References
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Proceedings ArticleDOI
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Proceedings Article
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