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
More filters
Journal ArticleDOI
Deep Learning Based Recommender System: A Survey and New Perspectives
TL;DR: A comprehensive review of recent research efforts on deep learning-based recommender systems is provided in this paper, along with a comprehensive summary of the state-of-the-art.
Proceedings Article
Deep One-Class Classification
Lukas Ruff,Robert A. Vandermeulen,Nico Goernitz,Lucas Deecke,Shoaib Ahmed Siddiqui,Alexander Binder,Emmanuel Müller,Marius Kloft +7 more
TL;DR: This paper introduces a new anomaly detection method—Deep Support Vector Data Description—, which is trained on an anomaly detection based objective and shows the effectiveness of the method on MNIST and CIFAR-10 image benchmark datasets as well as on the detection of adversarial examples of GTSRB stop signs.
Proceedings Article
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
TL;DR: The ViLBERT model as mentioned in this paper extends the BERT architecture to a multi-modal two-stream model, processing both visual and textual inputs in separate streams that interact through co-attentional transformer layers.
Journal ArticleDOI
Predicting Splicing from Primary Sequence with Deep Learning.
Kishore Jaganathan,Sofia Kyriazopoulou Panagiotopoulou,Jeremy F. McRae,Siavash Fazel Darbandi,David A. Knowles,Yang I. Li,Jack A. Kosmicki,Jack A. Kosmicki,Juan Arbelaez,Wenwu Cui,Grace Schwartz,Eric D. Chow,Efstathios Kanterakis,Hong Gao,Amirali Kia,Serafim Batzoglou,Stephen Sanders,Kyle Kai-How Farh +17 more
TL;DR: A deep neural network is described that accurately predicts splice junctions from an arbitrary pre-mRNA transcript sequence, enabling precise prediction of noncoding genetic variants that cause cryptic splicing.
Proceedings ArticleDOI
The “Something Something” Video Database for Learning and Evaluating Visual Common Sense
Raghav Goyal,Samira Ebrahimi Kahou,Vincent Michalski,Joanna Materzynska,Susanne Westphal,Heuna Kim,Valentin Haenel,Ingo Fruend,Peter N. Yianilos,Moritz Mueller-Freitag,Florian Hoppe,Christian Thurau,Ingo Bax,Roland Memisevic +13 more
TL;DR: This work describes the ongoing collection of the “something-something” database of video prediction tasks whose solutions require a common sense understanding of the depicted situation, and describes the challenges in crowd-sourcing this data at scale.
References
More filters
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.