Open AccessProceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
Reads0
Chats0
TLDR
This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small 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.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
Citations
More filters
Proceedings ArticleDOI
Deep Supervised Cross-Modal Retrieval
TL;DR: Deep Supervised Cross-modal Retrieval (DSCMR) aims to find a common representation space, in which the samples from different modalities can be compared directly and minimises the discrimination loss in both the label space and theCommon representation space to supervise the model learning discriminative features.
Posted Content
Wide Activation for Efficient and Accurate Image Super-Resolution.
TL;DR: This report demonstrates that with same parameters and computational budgets, models with wider features before ReLU activation have significantly better performance for single image super-resolution (SISR) and introduces linear low-rank convolution into SR networks to achieve even better accuracy-efficiency tradeoffs.
Journal ArticleDOI
Deep convolutional neural networks for diabetic retinopathy detection by image classification
Shaohua Wan,Yan Liang,Yin Zhang +2 more
TL;DR: This paper brings convolutional neural networks power to DR detection, which includes 3 major difficult challenges: classification, segmentation and detection, and adopts AlexNet, VggNet, GoogleNet, ResNet, and analyze how well these models do with the DR image classification.
Posted Content
Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics
TL;DR: This work presents a new large-scale challenging DeepFake video dataset, Celeb-DF, which contains 5,639 high-quality DeepFake videos of celebrities generated using improved synthesis process and conducts a comprehensive evaluation of DeepFake detection methods and datasets to demonstrate the escalated level of challenges posed by Celebrity-DF.
Posted Content
Quantifying the Carbon Emissions of Machine Learning
TL;DR: This work presents their Machine Learning Emissions Calculator, a tool for the community to better understand the environmental impact of training ML models and concrete actions that individual practitioners and organizations can take to mitigate their carbon emissions.
References
More filters
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
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
TL;DR: RCNN as discussed by the authors combines CNNs with bottom-up region proposals to localize and segment objects, and when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost.
Posted Content
Fully Convolutional Networks for Semantic Segmentation
TL;DR: It is shown that convolutional networks by themselves, trained end- to-end, pixels-to-pixels, improve on the previous best result in semantic segmentation.
Journal ArticleDOI
Backpropagation applied to handwritten zip code recognition
Yann LeCun,Bernhard E. Boser,John S. Denker,D. Henderson,Richard Howard,W. Hubbard,Lawrence D. Jackel +6 more
TL;DR: This paper demonstrates how constraints from the task domain can be integrated into a backpropagation network through the architecture of the network, successfully applied to the recognition of handwritten zip code digits provided by the U.S. Postal Service.
Journal ArticleDOI
The Pascal Visual Object Classes Challenge: A Retrospective
TL;DR: A review of the Pascal Visual Object Classes challenge from 2008-2012 and an appraisal of the aspects of the challenge that worked well, and those that could be improved in future challenges.