Siamese network is a neural network that contain two or more identical subnetwork. The purpose of this network is to find the similarity or comparing the relationship between two comparable things. Unlike classification task that uses cross entropy as the loss function, siamese network usually uses contrastive loss or triplet loss.
This project follows Hadsell-et-al.'06 [1] by computing the Euclidean distance on the output of the shared network and by optimizing the contrastive loss (see paper for more details).
[1] "Dimensionality Reduction by Learning an Invariant Mapping" http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
The input of these will be image_left, image_right and .
Our model uses 5 layer of convolutional layer and pooling followed. We do not use fully convolutonal net because convolution operation is faster on GPU(especially using CUDNN). See http://cs231n.github.io/convolutional-networks/#convert for more information on converting FC layer to Conv layer.
Train the model
git clone https://github.com/ardiya/siamesenetwork-tensorflow
python train.pyTensorboard Visualization(After training)
tensorboard --logdir=train.log- Update the API to 1.0
- Model provided is from iter-5000, the images below are trained until iter-50k
Result on MNIST Dataset:
See folder img to see the process until it converge, it is really fun to watch :)
Image retrieval uses the trained model to extract the features and get the most similar image using cosine similarity. See here
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Select id 865 in test image
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Retrieved top n similar image from train data with ids of [53144 47864 11074 51561 41350 34215 48182] from train data
