aravindhm/deep-goggle

Directory Structure

.
+-- core
|   +-- invert_nn.m - The core optimization lies here
+-- helpers - Several auxiliary functions that may be useful in general
+-- experiments - All the code to replicate our experiments
|   +-- networks
|   |   +-- hog_net.m - The hog and hogb networks are created using this
|   |   +-- dsift_net.m - The dense sift neural network is here
|   |   +-- Other networks used in our experiments can be downloaded from http://www.robots.ox.ac.uk/~aravindh/networks.html
|   +-- data
|   |   +-- hog/img.png - Image used for HOG and DSIFT qualitative results
|   |   +-- stock/ - Contains some more figures for reproducing qualitative results.
+-- ihog - either copy or soft link ihog from Vondrick et. al. This is required to run our experiments with hoggle.
+-- matconvnet - either copy or soft link matconvnet code here. If this is not here, then the setup function will not work.
+-- vlfeat - again either copy or soft copy. If this is not here, then the setup function will not work.

Experiments from the paper

To run the experiments used for our publication and replicate their results please follow the instructions below

Get the images

Download/soft link the imagenet validation images into experiments/data/imagenet12-val Download/soft link the stock abstrack images into experiments/data/stock

Compile ihog, vlfeat and matconvnet as per the instructions given at their respective webpages.

ihog: http://web.mit.edu/vondrick/ihog/

matconvnet: http://www.vlfeat.org/matconvnet/

vlfeat: http://www.vlfeat.org/

I) CNN experiments - qualitative results

cd experiments;
experiment_cnn;

This might run for several hours and generate a lot of matlab figures. Each figure contains the images used in the paper.

II) HOG, HOGle, DSIFT experiments - qualitative results

cd experiments;
experiment_shallow;

Same as before, it will generate matlab figures with the required images.

III) HOG, HOGb, HOGgle, DSIFT - quantitative results cd experiments; experiment_shallow_quantitative.m

It will generate mean and std of the normalized reconstruction error across 100 images. For this it will compute pre-images for 100 images and this will take a very long time.

Setting up and running your own networks

1. Create a network (net) that is compatible with matconvnet vl_simplenn function.
2. Run dg_setup.m in matlab
3. Run the network forward to generate a target reference representation y0
4. Call res = invert_nn(net, y0, [options]);
5. res.output{end} is the required reconstruction.