Chapter 1
Introduction to MatConvNet
MatConvNet is a MATLAB toolbox implementing Convolutional Neural Networks (CNN)
for computer vision applications. Since the breakthrough work of [8], CNNs have had a
major impact in computer vision, and image understanding in particular, essentially replacing
traditional image representations such as the ones implemented in our own VLFeat [13] open
source library.
While most CNNs are obtained by composing simple linear and non-linear filtering op-
erations such as convolution and rectification, their implementation is far from trivial. The
reason is that CNNs need to be learned from vast amounts of data, often millions of images,
requiring very efficient implementations. As most CNN libraries, MatConvNet achieves
this by using a variety of optimizations and, chiefly, by supporting computations on GPUs.
Numerous other machine learning, deep learning, and CNN open source libraries exist.
To cite some of the most popular ones: CudaConvNet,
1
Torch,
2
Theano,
3
and Caffe
4
. Many
of these libraries are well supported, with dozens of active contributors and large user bases.
Therefore, why creating yet another library?
The key motivation for developing MatConvNet was to provide an environment par-
ticularly friendly and efficient for researchers to use in their investigations.
5
MatConvNet
achieves this by its deep integration in the MATLAB environment, which is one of the most
popular development environments in computer vision research as well as in many other areas.
In particular, MatConvNet exposes as simple MATLAB commands CNN building blocks
such as convolution, normalisation and pooling (chapter 4); these can then be combined and
extended with ease to create CNN architectures. While many of such blocks use optimised
CPU and GPU implementations written in C++ and CUDA (section section 1.4), MATLAB
native support for GPU computation means that it is often possible to write new blocks
in MATLAB directly while maintaining computational efficiency. Compared to writing new
CNN components using lower level languages, this is an important simplification that can
significantly accelerate testing new ideas. Using MATLAB also provides a bridge towards
1
https://code.google.com/p/cuda-convnet/
2
http://cilvr.nyu.edu/doku.php?id=code:start
3
http://deeplearning.net/software/theano/
4
http://caffe.berkeleyvision.org
5
While from a user perspective MatConvNet currently relies on MATLAB, the library is being devel-
oped with a clean separation between MATLAB code and the C++ and CUDA core; therefore, in the future
the library may be extended to allow processing convolutional networks independently of MATLAB.
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