The process of learning good features for machine learning applications can be very computationally expensive and may prove difficult in cases where little data is available. A prototypical example of this is the one-shot learning setting, in which we must correctly make predictions given only a single example of each new class.

Here, I explored the power of One-Shot Learning with a popular model called "Siamese Neural Network".

• Setup

  • Clone or Download
  • Install Third-Party Dependency Requirements

• Architecture

  • Model
  • Learning

• Credits

• Contribution

• License (MIT)

Fire up your favorite command line utility (e.g. Terminal, iTerm or Command Prompt), and type the following commands to clone the project.

$ git clone https://github.com/victor-iyiola/few-shot-learning.git
$ cd few-shot-learning && ls
LICENSE        README.md      datasets       images         omniglot       one-shot.ipynb          utils.py

Or simply download this repository, and change your working directory to the downloaded project.

$ cd path/to/few-shot-learning
$ ls
LICENSE        README.md      datasets       images         omniglot       one-shot.ipynb          utils.py

Note: ls command will be changed to dir for Windows users.

This project was developed with Python v3.6.5. However any higher version of Python works fine.

• Jupyter >= v4.4.0
• NumPy >= v1.14.3
• Sci-kit learn >= v0.19.1
• Keras >= v2.2.0
• TensorFlow >= v1.9.0

$ pip3 install --upgrade -r requirements.txt
$ jupyter notebook
[I 08:36:52.271 LabApp] The Jupyter Notebook is running at:
[I 08:36:52.271 LabApp] http://localhost:8888/?token=cb246f438ca40a1a319d12c877d2e825c923fc0525c9d136
[I 08:36:52.271 LabApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
[C 08:36:52.273 LabApp]

    Copy/paste this URL into your browser when you connect for the first time,
    to login with a token:
        http://localhost:8888/?token=cb246f438ca40a1a319d12c877d2e825c923fc0525c9d136

A standard Siamese Convolutional Neural Network with $L$ layers each with $N_l$ units is used, where $h_{1, l}$ represents the hidden vector in layer $l$ for the first twin, and $h_{2,l}$ denotes the same for the second twin. Rectified Linear (ReLU) Units is exclusively used in the first $L-2$ layers and sigmoidal units in the remaining layers.

The model consists of a sequence of convolutional layers, each of which uses a single channel with filters of varying size and a fixed stride of 1. The number of convolutional filters is specified as a multiple of 16 to optimize performance. The network applies a ReLU activation function to the output feature maps, optionally followed by max-pooling with a filter size and stride of 2. Thus the $k^{th}$ filter map in each layer takes the following form:

$$ a^{(k)}{1, m} = \textrm{max-pool}(max(0, W^{(k)}{l-1} \star h_{1, (l-1)} + b_l), 2) $$

$$ a^{(k)}{2, m} = \textrm{max-pool}(max(0, W^{(k)}{l-1} \star h_{2, (l-1)} + b_l), 2) $$

where $W^{l-1, l}$ is the 3-dimensional tensor representing the feature maps for layer $l$ and $\star$ is the valid convolutional operation corresponding to returning only those output units which were the result of complete overlap between each convolutional filter and the input feature maps.

Loss function. Let $M$ represent the mini-batch size, where $i$ indexes the $i^{th}$ mini-batch. Now let $y(x^{(i)}_1, x^{(i)}_2)$ be a length-$M$ vector which contains the labels for the mini-batch, where $y(x^{(i)}_1, x^{(i)}_2) = 1$ whenever $x_1$ and $x_2$ are from the same character class and $y(x^{(i)}_1, x^{(i)}_2) = 0$ otherwise. A regularized cross-entropy objective is imposed on a binary classifier of the following form:

$$ \ell(x^{(i)}_1, x^{(i)}_2) = y(x^{(i)}_1, x^{(i)}_2)\log{p(x^{(i)}_1, x^{(i)}_2)} + (1 - y(x^{(i)}_1, x^{(i)}_2)) \log{(1 - p(x^{(i)}_1, x^{(i)}_2))} + \lambda^T\big|w\big|^2 $$

• Fellowship.ai
• Omniglot Dataset
• Siamese Neural Networks for One-shot Image Recognition

You are very welcome to modify and use them in your own projects.

Please keep a link to the original repository. If you have made a fork with substantial modifications that you feel may be useful, then please open a new issue on GitHub with a link and short description.

This project is opened under the MIT 2.0 License which allows very broad use for both academic and commercial purposes.

A few of the images used for demonstration purposes may be under copyright. These images are included under the "fair usage" laws.