飞翔 2019-05-31 17:22

Very Deep Convolutional Networks for Large-Scale Image Recognition

辉仔 2018-01-10 10:31
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 in

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

辉仔 2018-01-10 10:49
Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the trai


阿小庆 2018-06-24 17:11

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

Yann LeCun推荐!自监督学习、全景FPN...内容平台的四大技术指南

AI科技大本营 2019-05-06 10:51
Facebook CTO Mike Schroepfer 和 Facebook AI 部门的研究科学家 Manohar Paluri 在近日的 F8 大会主题演讲
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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

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

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

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

辉仔 2018-01-10 11:09
In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications. Comparatively, unsupervised learning with CNNs has received less attenti