This code was used to produce results reported in the following paper:
Maksim Lapin, Bernt Schiele and Matthias Hein
Scalable Multitask Representation Learning for Scene Classification
In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014
The software was tested on Debian GNU/Linux 7.4 (wheezy) using MATLAB R2013a and GCC 4.4.
git clone https://github.com/mlapin/cvpr14mtl.git
At MATLAB prompt:
showresults
Download the precomputed kernels:
cd matlab && make playkernels
At MATLAB prompt:
playground
You may need to recompile the STL-SDCA and MTL-SDCA solvers, see below for instructions.
To download more kernels (excluding the ones from Xiao et al.), run:
cd matlab && make allkernels
cd mtlsdca && make clean && make
If MATLAB is not found, edit the Makefile
and set the path manually,
e.g. MATLAB_PATH = /usr/lib/matlab-8.1
If Intel MKL is installed, specify the corresponding path in INTEL_MKL_PATH
;
otherwise, MATLAB BLAS will be used.
To disable verbose output from the solvers,
comment out the following line in the Makefile and recompile:
STD_CXXFLAGS += -DVERBOSE
cd usps && make
This will compile MATLAB code experiments.m
and create a text file cmd_experiments.txt
with commands that can be executed in parallel.
MCR environment needs to be set up to run the commands,
see run_experiments.sh
for details.
To learn more about working with the compiled MATLAB code, visit
http://www.mathworks.com/help/compiler/working-with-the-mcr.html
First, create the 10 splits.
Go to matlab/splits
and run at MATLAB prompt:
splits
Next, have a look at the Makefile
:
cd matlab && make
This will show a list of available make targets.
Note: you must modify the Makefile
.
At the very minimum, you must specify:
SUN397 =
the path to the downloaded SUN397 dataset;SUN397R100K =
the path to a directory where the processed (resized) images will be stored;- arguments to the
make/make_cmd_[mtl]experiments.sh
scripts, seeex
andexmtl
make targets.
To resize images to at most 100K pixels, run
make r100k
To run single task learning (STL) experiments, use
make ex
This will compile MATLAB code and create a number of text files with commands that can be executed in parallel. As with the USPS/MNIST experiments, MCR environment needs to be set up to run the commands.
Similarly, to run multitask learning (MTL) experiments, use
make exmtl
Note: all results (precomputed kernel matrices, trained models,
test scores, etc.) will be stored in matlab/experiments
and will require disk space on the order of 500-700GB.
By default, caching of image descriptors (Fisher Vector) is disabled
(doNotCacheDescriptors = true
in matlab/recognition/traintest.m
)
and only the kernel matrices are saved to disk.
Otherwise, the disk space requirements increase to up to 3-4TB.