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

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

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

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

Fully Convolutional Networks for Semantic Segmentation

辉仔 2018-01-10 10:46
Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-the-a

A Neural Algorithm of Artistic Style

dwSun 2018-02-07 17:07
In fine art, especially painting, humans have mastered the skill to create unique visual experiences through composing a complex interplay between the content and style of an image. Thus far the algor

Dynamic Routing Between Capsules

dwSun 2018-02-06 16:16
A capsule is a group of neurons whose activity vector represents the instantiation parameters of a specific type of entity such as an object or an object part. We use the length of the activity vector

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

Learning to Segment Every Thing

dwSun 2018-01-15 20:39
Existing methods for object instance segmentation require all training instances to be labeled with segmentation masks. This requirement makes it expensive to annotate new categories and has restricte

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
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