update text

app.py

@@ -23,18 +23,22 @@ def gen(md, model_name, seed, nb_iter, nb_samples, width, height, nb_active, onl
         w=int(width), h=int(height), c=1, 
         batch_size=bs,
     )
+    if not black_bg:
+        samples = 1 - samples
     if only_last:
         s = int(math.sqrt((nb_samples)))
         grid = grid_of_images_default(samples[-1].numpy(), shape=(s, s)) 
     else:
         grid = grid_of_images_default(samples.reshape((samples.shape[0]*samples.shape[1], int(height), int(width), 1)).numpy(), shape=(samples.shape[0], samples.shape[1])) 
-    if not black_bg:
-        grid = 1 - grid
     grid = (grid*255).astype("uint8")
     return Image.fromarray(grid)
 
 text = """
-Interface with ConvAE model (from [here](https://arxiv.org/pdf/1606.04345.pdf)) and DeepConvAE model (from [here](https://tel.archives-ouvertes.fr/tel-01838272/file/75406_CHERTI_2018_diffusion.pdf), Section 10.1 with `L=3`), Dense K-Sparse model (from [here](https://openreview.net/forum?id=r1QXQkSYg))
+This interface supports generation of samples from:
+
+- ConvAE model (from [`Digits that are not: Generating new types through deep neural nets`](https://arxiv.org/pdf/1606.04345.pdf))
+- DeepConvAE model (from [here](https://tel.archives-ouvertes.fr/tel-01838272/file/75406_CHERTI_2018_diffusion.pdf), Section 10.1 with `L=3`)
+- Dense K-Sparse model (from [`Out-of-class novelty generation`](https://openreview.net/forum?id=r1QXQkSYg))
 
 These models were trained on MNIST only (digits), but were found to generate new kinds of symbols, see the references for more details.