Open AccessProceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
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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
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Deep Learning for Classification of Colorectal Polyps on Whole-slide Images.
Bruno Korbar,Andrea M. Olofson,Allen P. Miraflor,Catherine M. Nicka,Matthew A. Suriawinata,Lorenzo Torresani,Arief A. Suriawinata,Saeed Hassanpour +7 more
TL;DR: An automatic image analysis method that can accurately classify different types of colorectal polyps on whole-slide images to help pathologists with this characterization and diagnosis and can reduce the cognitive burden on pathologists and improve their efficacy in histopathological characterization and follow-up recommendations.
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Synthesizing Images of Humans in Unseen Poses
TL;DR: In this article, a generative neural network is proposed to synthesize unseen human poses from human action videos. But their work is limited to three action classes: golf, yoga/workouts and tennis.
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Semi-Supervised Domain Adaptation via Minimax Entropy
TL;DR: In this paper, a novel Minimax Entropy (MME) approach that adversarially optimizes an adaptive few-shot model is proposed to align feature distributions of source and target domains without any target supervision.
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Deep convolutional autoencoder for radar-based classification of similar aided and unaided human activities
TL;DR: A three-layer, deep convolutional autoencoder (CAE) is proposed, which utilizes unsupervised pretraining to initialize the weights in the subsequent Convolutional layers, and is shown to be more effective than other deep learning architectures.
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CirCNN: accelerating and compressing deep neural networks using block-circulant weight matrices
Caiwen Ding,Siyu Liao,Yanzhi Wang,Zhe Li,Ning Liu,Youwei Zhuo,Chao Wang,Xuehai Qian,Yu Bai,Geng Yuan,Xiaolong Ma,Yipeng Zhang,Jian Tang,Qinru Qiu,Xue Lin,Bo Yuan +15 more
TL;DR: The CirCNN architecture is proposed, a universal DNN inference engine that can be implemented in various hardware/software platforms with configurable network architecture (e.g., layer type, size, scales, etc) and FFT can be used as the key computing kernel which ensures universal and small-footprint implementations.
References
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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.
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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.
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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.
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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.