Deep Residual Learning for Image Recognition paper availability about faster_rcnn HOT 11 CLOSED

The above residual network example of tensorflow has been moved here

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The new architecture is so simple to implement and the results are so strong that you might win the ICCV Best Paper Award (Marr Prize). It is certain that all types of deep neural network models will adopt residual learning and gain consistent performance improvements very soon.

Unfortunately, the paper did not mention the details of your segmentation algorithm. We will have to wait for another paper focus on that topic.

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https://github.com/tensorflow/skflow/blob/master/examples/resnet.py

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The paper can be found in http://arxiv.org/abs/1512.03385.

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@ShaoqingRen any hint on implementing it using caffe? @futurely

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Several examples are available in MXNet, Keras and Lasagne. apache/mxnet#931

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Caffe: https://github.com/beijbom/beijbom_python_lib/blob/master/beijbom_caffe_tools.py

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@futurely (and anyone else): is there a TensorFlow implementation?

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A warning: Quite a few of these implementations seem to be adding ReLU after every convolution layer. That doesn't seem right to me. If your building block is y = ReLU(f(x)) + x, then every layer will only increase y and never decrease y because one of the terms of the addition is constrained to be nonnegative.

The original paper only uses ReLU just after the addition y = ReLU(f(x) + x), and right after the first (but not the second) convolution layer.

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@gcr good point

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I want to use tensorflow to complish the residual network of MSAR. Who can give me some code support ! Not the paper ! Thx!

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