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
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Posted Content
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
Xintao Wang,Ke Yu,Shixiang Wu,Jinjin Gu,Yihao Liu,Chao Dong,Chen Change Loy,Yu Qiao,Xiaoou Tang +8 more
TL;DR: This work thoroughly study three key components of SRGAN – network architecture, adversarial loss and perceptual loss, and improves each of them to derive an Enhanced SRGAN (ESRGAN), which achieves consistently better visual quality with more realistic and natural textures than SRGAN.
Posted Content
Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
TL;DR: This article proposed an adaptive attention model with a visual sentinel to decide whether to attend to the image and where, in order to extract meaningful information for sequential word generation, which set the new state-of-the-art by a significant margin.
Book ChapterDOI
DeeperCut: A Deeper, Stronger, and Faster Multi-person Pose Estimation Model
Eldar Insafutdinov,Leonid Pishchulin,Bjoern Andres,Mykhaylo Andriluka,Mykhaylo Andriluka,Bernt Schiele +5 more
TL;DR: In this article, the authors proposed an improved body part detector that generates effective bottom-up proposals for body parts, image-conditioned pairwise terms that allow to assemble the proposals into a variable number of consistent body part configurations, and an incremental optimization strategy that explores the search space more efficiently thus leading both to better performance and significant speedup factors.
Proceedings ArticleDOI
Neural Factorization Machines for Sparse Predictive Analytics
Xiangnan He,Tat-Seng Chua +1 more
TL;DR: Neural Factorization Machines (NFM) as discussed by the authors is a special case of NFM without hidden layers, which combines the linearity of FM in modelling second-order feature interactions and the non-linearity of neural network in modelling higher-order features.
Journal ArticleDOI
Quantum Chemistry in the Age of Quantum Computing.
Yudong Cao,Jonathan Romero,Jonathan P. Olson,Matthias Degroote,Matthias Degroote,Peter D. Johnson,Mária Kieferová,Mária Kieferová,Ian D. Kivlichan,Tim Menke,Tim Menke,Borja Peropadre,Nicolas P. D. Sawaya,Sukin Sim,Libor Veis,Alán Aspuru-Guzik +15 more
TL;DR: This Review provides an overview of the algorithms and results that are relevant for quantum chemistry and aims to help quantum chemists who seek to learn more about quantum computing and quantum computing researchers who would like to explore applications in quantum chemistry.
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.
Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
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.
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).
Proceedings Article
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe,Christian Szegedy +1 more
TL;DR: Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin.