Backpropagation for training Siamese nets

[YangXS]

I find that for training Siamese nets[ref1], a contrastive loss layer was added. No extra code was added for backpropagation in other layers. Is that right? However, according to [ref2] where a similar net was proposed for metric learning, the backpropagation of other layers should also be revised. At least the subtraction of each layer's outputs of the input pair should be added to compute the gradient w.r.t the wieght and bias. If I missed the revision of backpropagation, please help me. Thanks.
[ref1] Sumit Chopra, Raia Hadsell, Yann LeCun. Learning a Similarity Measure Discriminatively with Applications to Face Verification. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Diego CA, June 2005.
[ref2] Junlin Hu, Jiwen Lu, Yap-Peng Tan. Discriminative Deep Metric Learning for Face Verification in the Wild. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2014

[YangXS]

Sorry for this misunderstanding. The gradients descent method in [ref2] is actually the same as the one implemented for training Siamese nets.

[shelhamer]

The ContrastiveLossLayer in Caffe is suitable for training siamese networks. If an alternative gradient computation is used in [2], you will need to implement it.

[nemohz]

Have you implement Discriminative Deep Metric Learning for Face Verification in the Wild? I try to implement it but I can not achieve the performance of the paper.

[YangXS]

Practically, the model proposed in Discriminative Deep Metric Learning for
Face Verification in the Wild (DDML) can be implemented by
the Siamese network of Caffe. I have successfully trained the Siamese
network for my task. Though there may be some differences in
the implementation, such as the DDML do not use the convolution, I think
the Siamese network provided in Caffe is more effective.

2015-06-19 11:23 GMT+08:00 nansea notifications@github.com:

Have you implement Discriminative Deep Metric Learning for Face
Verification in the Wild? I try to implement it but I can not achieve the
performance of the paper.

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Reply to this email directly or view it on GitHub
#1265 (comment).

[xuzhm]

@YangXS Hi,I also use Caffe to train the Siamese network for my task, but it seems not converge to a good result, can you help me?

[YangXS]

@anxiaoxi45, Could you give more details about your task ? I really spend lots of time to make it work.

[keerthanashanmugam]

@YangXS Could you provide the model parameters (no of nodes in each of the layers ) which achieves the results (for LBP features for deep and shallow network ) in Discriminative Deep Metric Learning for Face Verification in the Wild (DDML)? And did you normalise the data, if so how.