Rethinking the Inception Architecture for Computer Vision

辉仔 2018-01-10 11:10
Convolutional networks are at the core of most state-of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yieldin

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

jixiaohui 2018-01-10 14:52
We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to

OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks

辉仔 2018-01-10 10:21
We present an integrated framework for using Convolutional Networks for classification, localization and detection. We show how a multiscale and sliding window approach can be efficiently implemented

Learning Transferable Architectures for Scalable Image Recognition

jixiaohui 2018-01-10 15:36
Developing neural network image classification models often requires significant architecture engineering. In this paper, we attempt to automate this engineering process by learning the model architec

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

jixiaohui 2018-01-10 11:37
Very deep convolutional networks have been central to the largest advances in image recognition performance in recent years. One example is the Inception architecture that has been shown to achieve ve

One weird trick for parallelizing convolutional neural networks

辉仔 2018-01-10 10:26
I present a new way to parallelize the training of convolutional neural networks across multiple GPUs. The method scales significantly better than all alternatives when applied to modern convolutional

Deep Residual Learning for Image Recognition

jixiaohui 2018-01-10 11:15
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 re

Residual Attention Network for Image Classification

dwSun 2018-01-15 20:43
In this work, we propose "Residual Attention Network", a convolutional neural network using attention mechanism which can incorporate with state-of-art feed forward network architecture in an end-to-e

Going Deeper with Convolutions

辉仔 2018-01-10 11:12
We propose a deep convolutional neural network architecture codenamed "Inception", which was responsible for setting the new state of the art for classification and detection in the ImageNet Large-Sca

Mask R-CNN

dwSun 2018-01-15 20:45
We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality
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