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0. Referencehttps://arxiv.org/abs/1602.07261 Inception-v4, Inception-ResNet and the Impact of Residual Connections on LearningVery 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 very good performance at relatively low computational costarxiv.org1. Intr..
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0. Referencehttps://proceedings.mlr.press/v9/glorot10a.html Understanding the difficulty of training deep feedforward neural networksWhereas before 2006 it appears that deep multi-layer neural networks were not successfully trained, since then several algorithms have been shown to successfully train them, with experimental resul...proceedings.mlr.press1. Introduction- 본 논문은 Xavier Initialization..
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0. Referencehttps://arxiv.org/abs/1502.01852 Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet ClassificationRectified activation units (rectifiers) are essential for state-of-the-art neural networks. In this work, we study rectifier neural networks for image classification from two aspects. First, we propose a Parametric Rectified Linear Unit (PReLU) that generaarxiv...
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0. Referencehttps://arxiv.org/abs/1512.03385 Deep Residual Learning for Image RecognitionDeeper 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 reformulate the layers as learning residual functions witharxiv.org1. Introduction- 해당 논문은 "더 성능이 좋은 Network를 만..
