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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SIFT Meets CNN: A Decade Survey of Instance Retrieval
Liang Zheng,Yi Yang,Qi Tian +2 more
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Fast is better than free: Revisiting adversarial training
TL;DR: It is made the surprising discovery that it is possible to train empirically robust models using a much weaker and cheaper adversary, an approach that was previously believed to be ineffective, rendering the method no more costly than standard training in practice.
Proceedings ArticleDOI
Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image
Fangchang Mal,Sertac Karaman +1 more
TL;DR: Sparse-to-dense as discussed by the authors uses a single deep regression network to learn directly from the RGB-D raw data, and explore the impact of number of depth samples on prediction accuracy.
Proceedings Article
Pose Guided Person Image Generation
TL;DR: Zhang et al. as discussed by the authors proposed a pose guided person generation network (PG$^2$) that allows to synthesize person images in arbitrary poses, based on an image of that person and a novel pose.
Posted Content
On Detecting Adversarial Perturbations
TL;DR: It is shown empirically that adversarial perturbations can be detected surprisingly well even though they are quasi-imperceptible to humans.
References
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Proceedings Article
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Proceedings Article
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Proceedings ArticleDOI
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Proceedings Article
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