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全卷积文献中文翻译版
Convolutional networks are powerful visual models that
yield hierarchies of features. We show that convolutional
networks by themselves, trained end-to-end, pixelsto-
pixels, exceed the state-of-the-art in semantic segmentation.
Our key insight is to build “fully convolutional”
networks that take input of arbitrary size and produce
correspondingly-sized output with efficient inference and
learning. We define and detail the space of fully convolutional
networks, explain their application to spatially dense
prediction tasks, and draw connections to prior models. We
adapt contemporary classification networks (AlexNet [22],
the VGG net [34], and GoogLeNet [35]) into fully convolutional
networks and transfer their learned representations
by fine-tuning [5] to the segmentation task
2018-05-31
深度学习参考文献
Improving information flow in deep networks helps to
ease the training difficulties and utilize parameters more
efficiently. Here we propose a new convolutional neural
network architecture with alternately updated clique
(CliqueNet). In contrast to prior networks, there are both
forward and backward connections between any two layers
in the same block. The layers are constructed as a loop and
are updated alternately. The CliqueNet has some unique
properties. For each layer, it is both the input and output of
any other layer in the same block, so that the information
flow among layers is maximized.
2018-05-27
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