diff --git a/artifacts/results/mnist_vae.jls b/artifacts/results/mnist_vae.jls
index 1beb8495d7cc6c0d2a30194edf20c3e28b6625f4..67e2b2de0951f2d914bb4a75088c8ced1e118dbc 100644
Binary files a/artifacts/results/mnist_vae.jls and b/artifacts/results/mnist_vae.jls differ
diff --git a/artifacts/results/mnist_vae_weak.jls b/artifacts/results/mnist_vae_weak.jls
index bb3131c46c4fd99db0cb695160b97bc4519e5d0f..ecccbf390b1e0764896dc835e21633d9bb8b9c25 100644
Binary files a/artifacts/results/mnist_vae_weak.jls and b/artifacts/results/mnist_vae_weak.jls differ
diff --git a/notebooks/mnist.qmd b/notebooks/mnist.qmd
index 99eb283a4994af1fc6ffc3f02c29ca2f9c37904b..4fdd65f540528b208e86fe72ca5607a8df733f33 100644
--- a/notebooks/mnist.qmd
+++ b/notebooks/mnist.qmd
@@ -137,15 +137,16 @@ savefig(plt, joinpath(output_images_path, "surrogate_gone_wrong.png"))
 ```{julia}
 function pre_process(x; noise::Float32=0.03f0)
     ϵ = Float32.(randn(size(x)) * noise)
-    x = @.(2 * x - 1) .+ ϵ
+    # x = @.(2 * x - 1) 
+    x += ϵ
     return x
 end
 ```
 
 ```{julia}
 # Hyper:
-_retrain = false
-_regen = false
+_retrain = true
+_regen = true
 
 # Data:
 n_obs = 10000
@@ -179,7 +180,7 @@ _finaliser = x -> x                         # finaliser function
 
 ```{julia}
 # JEM parameters:
-𝒟x = Uniform(-1,1)
+𝒟x = Uniform(0,1)
 𝒟y = Categorical(ones(output_dim) ./ output_dim)
 sampler = ConditionalSampler(
     𝒟x, 𝒟y, 
@@ -306,7 +307,7 @@ model_performance = DataFrame()
 for (mod_name, mod) in model_dict
     # Test performance:
     test_data = load_mnist_test()
-    test_data.X = pre_process.(test_data.X, noise=0.0f0)
+    # test_data.X = pre_process.(test_data.X, noise=0.0f0)
     _perf = CounterfactualExplanations.Models.model_evaluation(mod, test_data, measure=collect(values(measure)))
     _perf = DataFrame([[p] for p in _perf], collect(keys(measure)))
     _perf.mod_name .= mod_name
diff --git a/paper/paper.pdf b/paper/paper.pdf
index ca87169a5b0c6e9867f9fbce9a18262852f3c448..7f8f4cc72ad9d371e1c9c82e43d7b19bde004d3f 100644
Binary files a/paper/paper.pdf and b/paper/paper.pdf differ
diff --git a/paper/paper.tex b/paper/paper.tex
index 32de8391f5f72c02f99e6e6f05f53fc15f133328..648a2cc3f08e4f355184397622aeb2beee5c90d3 100644
--- a/paper/paper.tex
+++ b/paper/paper.tex
@@ -310,6 +310,7 @@ As noted by \citet{guidotti2022counterfactual}, these distance-based measures ar
   \item ECCCo is sensitive to optimizer (Adam works well), learning rate and distance metric (l1 currently only one that works)
   \item SGLD takes time 
   \item REVISE has benefit of lower dimensional space
+  \item For MNIST it seems that ECCCo is better at reducing pixel values than increasing them (better at erasing than writing)
 \end{itemize}
 
 \section{Discussion}