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Thank you for providing the Gehler-Shi data (your repo may be the last public source of it!). The Gehler-Shi website is no longer providing zip links.

When I reviewed the code in internal/TrainModel.m, line 283 ~ 288, the original codes are as below:

if params.DEEP.WHITEN_FEATURES
      % Unwhiten the first layer according to the whitening transformation, so
      % that is produces the correct output on the unwhitened feature vectors.
      model.W{1} = model.W{1} * whitening_transformation.A;
      model.b{1} = model.W{1} * whitening_transformation.b + model.b{1};
end

Since model.W{1} changed before model.b{1} it will lead a computational mistake to model.b{1}.And this mistake may weaken the model performance.
So I simply change the code order as below:

      model.b{1} = model.W{1} * whitening_transformation.b + model.b{1};
      model.W{1} = model.W{1} * whitening_transformation.A;

And after this change, I got a significant improvement in my experiment.^.^

hi，
@jonbarron, thanks for sharing the codes of the ffcc. I can't understand the meaning of the conv operations in the function "RenderHistorgramGaussian". The code is as follow,

% Threshold the mahalanobis distance at 3 to make a binary mask, which is
% dilated to produce an ellipse with a dot in the center.
mask = mahal_dist <= 3;
prediction = (conv2(double(mask), ones(3,3), 'same') > 0) & ~mask;
prediction = prediction | (conv2(double(mahal_dist == min(mahal_dist(:))), [0, 1, 0; 1, 1, 1; 0, 1, 0], 'same') > 0);

% Optionally create a mask with the ground-truth white point rendered as a dot.
if ~isempty(mu_true)
  D = (us - mu_true(1)).^2 + (vs - mu_true(2)).^2;
  truth = D == min(D(:));
  truth = (conv2(double(truth), [0, 1, 0; 1, 1, 1; 0, 1, 0], 'same') > 0);
  truth = (conv2(double(truth), [0, 1, 0; 1, 1, 1; 0, 1, 0], 'same') > 0);

I want to ask the questions,

1. What's the meaning of the function conv2(double(mask), ones(3,3), 'same') > 0) ?
2. I can understand the formula about mahal_dist == min(mahal_dist(:) to get the ellipse of the minmum distance. However, i can't understand why to run the conv operation of the formula conv2(double(mahal_dist == min(mahal_dist(:))), [0, 1, 0; 1, 1, 1; 0, 1, 0], 'same') and why to chose the matrix [0, 1, 0; 1, 1, 1; 0, 1, 0] .
3. Why are the two same conv operations with conv2(double(truth), [0, 1, 0; 1, 1, 1; 0, 1, 0], 'same') ?

Look forward to your favourable reply !
@jonbarron

hi,
When i set the params.DEEP.ENABLED true, it apperas the bug about the length(params.DEEP.FEATURE_FILENAME_TAGS). The define of the params.DEEP.FEATURE_FILENAME_TAGS is 'feature'.

Hi,
@jonbarron , thanks for sharing the ffcc project.
I am researching the ffcc project and have met two problems as follows.
First, I can not understand why use the MaskedLocalAbsoluteDeviation to compute the im_channels{2}, and i debug the computational process of the im_channel.

function im_channels = ChannelizeImage(im, mask)
% Generate feature images (color and gradient) and combine them into
% different cell channels.

assert(isa(mask, 'logical'));
im_channels = {};

im_channels{1} = cast(bsxfun(@times, double(im), mask), 'like', im);
im_channels{2} = cast(MaskedLocalAbsoluteDeviation(im, mask), 'like', im);

im_channels = ChannelizeImage(im, mask);  %  256*384*3, 256*384*3
......                                   % histrogram feature 
X = Xc ;                                 % 64*64*2
......                                   % get the fourier transformation(F_fft and X_fft) of the model param and X, 
FX_fft = sum(bsxfun(@times, X_fft, F_fft), 3)  

Second, how to understand the computational preconditioner.F_fft * model_precond.F_fft to get the model.F_fft. Why not just use the variable model_precond as model.F_fft. I disconver the values of the variable preconditioner.F_fft are approximate zeros.

% the computational process of the preconditioner.F_fft  
u_variation_fft = abs(fft2([-1; 1]/sqrt(8), X_sz(1), X_sz(2))).^2;
v_variation_fft = abs(fft2([-1, 1]/sqrt(8), X_sz(1), X_sz(2))).^2;
total_variation_fft = u_variation_fft + v_variation_fft;

% A helper function for applying a scale and shift to a stack of images.
apply_scale_shift = @(x, m, b)bsxfun(@plus, bsxfun(@times, x, permute(m(:), [2,3,1])), permute(b(:), [2,3,1]));

@jonbarron，i will be looking forward to your reply！Thanks!