diff --git a/prototorch/models/vis.py b/prototorch/models/vis.py
index 6099bc3..7f21e48 100644
--- a/prototorch/models/vis.py
+++ b/prototorch/models/vis.py
@@ -269,6 +269,7 @@ class Vis2DAbstract(pl.Callback):
                  cmap="viridis",
                  border=1,
                  resolution=50,
+                 show_protos=True,
                  tensorboard=False,
                  show_last_only=False,
                  pause_time=0.1,
@@ -288,11 +289,17 @@ class Vis2DAbstract(pl.Callback):
         self.cmap = cmap
         self.border = border
         self.resolution = resolution
+        self.show_protos = show_protos
         self.tensorboard = tensorboard
         self.show_last_only = show_last_only
         self.pause_time = pause_time
         self.block = block
 
+    def precheck(self, trainer):
+        if self.show_last_only:
+            if trainer.current_epoch != trainer.max_epochs - 1:
+                return
+
     def setup_ax(self, xlabel=None, ylabel=None):
         ax = self.fig.gca()
         ax.cla()
@@ -312,6 +319,28 @@ class Vis2DAbstract(pl.Callback):
         mesh_input = np.c_[xx.ravel(), yy.ravel()]
         return mesh_input, xx, yy
 
+    def plot_data(self, ax, x, y):
+        ax.scatter(
+            x[:, 0],
+            x[:, 1],
+            c=y,
+            cmap=self.cmap,
+            edgecolor="k",
+            marker="o",
+            s=30,
+        )
+
+    def plot_protos(self, ax, protos, plabels):
+        ax.scatter(
+            protos[:, 0],
+            protos[:, 1],
+            c=plabels,
+            cmap=self.cmap,
+            edgecolor="k",
+            marker="D",
+            s=50,
+        )
+
     def add_to_tensorboard(self, trainer, pl_module):
         tb = pl_module.logger.experiment
         tb.add_figure(tag=f"{self.title}",
@@ -330,115 +359,89 @@ class Vis2DAbstract(pl.Callback):
 
 class VisGLVQ2D(Vis2DAbstract):
     def on_epoch_end(self, trainer, pl_module):
-        if self.show_last_only:
-            if trainer.current_epoch != trainer.max_epochs - 1:
-                return
+        self.precheck(trainer)
+
         protos = pl_module.prototypes
         plabels = pl_module.prototype_labels
         x_train, y_train = self.x_train, self.y_train
         ax = self.setup_ax(xlabel="Data dimension 1",
                            ylabel="Data dimension 2")
-        ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor="k")
-        ax.scatter(
-            protos[:, 0],
-            protos[:, 1],
-            c=plabels,
-            cmap=self.cmap,
-            edgecolor="k",
-            marker="D",
-            s=50,
-        )
+        self.plot_data(ax, x_train, y_train)
+        self.plot_protos(ax, protos, plabels)
         x = np.vstack((x_train, protos))
         mesh_input, xx, yy = self.get_mesh_input(x)
         y_pred = pl_module.predict(torch.Tensor(mesh_input))
         y_pred = y_pred.reshape(xx.shape)
-
         ax.contourf(xx, yy, y_pred, cmap=self.cmap, alpha=0.35)
-        # ax.set_xlim(left=x_min + 0, right=x_max - 0)
-        # ax.set_ylim(bottom=y_min + 0, top=y_max - 0)
 
         self.log_and_display(trainer, pl_module)
 
 
 class VisSiameseGLVQ2D(Vis2DAbstract):
+    def __init__(self, *args, map_protos=True, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.map_protos = map_protos
+
     def on_epoch_end(self, trainer, pl_module):
+        self.precheck(trainer)
+
         protos = pl_module.prototypes
         plabels = pl_module.prototype_labels
         x_train, y_train = self.x_train, self.y_train
         x_train = pl_module.backbone(torch.Tensor(x_train)).detach()
-        protos = pl_module.backbone(torch.Tensor(protos)).detach()
+        if self.map_protos:
+            protos = pl_module.backbone(torch.Tensor(protos)).detach()
         ax = self.setup_ax()
-        ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor="k")
-        ax.scatter(
-            protos[:, 0],
-            protos[:, 1],
-            c=plabels,
-            cmap=self.cmap,
-            edgecolor="k",
-            marker="D",
-            s=50,
-        )
-        x = np.vstack((x_train, protos))
-        mesh_input, xx, yy = self.get_mesh_input(x)
+        self.plot_data(ax, x_train, y_train)
+        if self.show_protos:
+            self.plot_protos(ax, protos, plabels)
+            x = np.vstack((x_train, protos))
+            mesh_input, xx, yy = self.get_mesh_input(x)
+        else:
+            mesh_input, xx, yy = self.get_mesh_input(x_train)
         y_pred = pl_module.predict_latent(torch.Tensor(mesh_input))
         y_pred = y_pred.reshape(xx.shape)
-
         ax.contourf(xx, yy, y_pred, cmap=self.cmap, alpha=0.35)
-        # ax.set_xlim(left=x_min + 0, right=x_max - 0)
-        # ax.set_ylim(bottom=y_min + 0, top=y_max - 0)
 
         self.log_and_display(trainer, pl_module)
 
 
 class VisCBC2D(Vis2DAbstract):
     def on_epoch_end(self, trainer, pl_module):
+        self.precheck(trainer)
+
         x_train, y_train = self.x_train, self.y_train
         protos = pl_module.components
         ax = self.setup_ax(xlabel="Data dimension 1",
                            ylabel="Data dimension 2")
-        ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor="k")
-        ax.scatter(
-            protos[:, 0],
-            protos[:, 1],
-            c="w",
-            cmap=self.cmap,
-            edgecolor="k",
-            marker="D",
-            s=50,
-        )
+        self.plot_data(ax, x_train, y_train)
+        self.plot_protos(ax, protos, plabels)
         x = np.vstack((x_train, protos))
         mesh_input, xx, yy = self.get_mesh_input(x)
         y_pred = pl_module.predict(torch.Tensor(mesh_input))
         y_pred = y_pred.reshape(xx.shape)
 
         ax.contourf(xx, yy, y_pred, cmap=self.cmap, alpha=0.35)
-        # ax.set_xlim(left=x_min + 0, right=x_max - 0)
-        # ax.set_ylim(bottom=y_min + 0, top=y_max - 0)
 
         self.log_and_display(trainer, pl_module)
 
 
 class VisNG2D(Vis2DAbstract):
     def on_epoch_end(self, trainer, pl_module):
+        self.precheck(trainer)
+
         x_train, y_train = self.x_train, self.y_train
         protos = pl_module.prototypes
         cmat = pl_module.topology_layer.cmat.cpu().numpy()
 
         ax = self.setup_ax(xlabel="Data dimension 1",
                            ylabel="Data dimension 2")
-        ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor="k")
-        ax.scatter(
-            protos[:, 0],
-            protos[:, 1],
-            c="k",
-            edgecolor="k",
-            marker="D",
-            s=50,
-        )
+        self.plot_data(ax, x_train, y_train)
+        self.plot_protos(ax, protos, "w")
 
         # Draw connections
         for i in range(len(protos)):
-            for j in range(len(protos)):
+            for j in range(i, len(protos)):
                 if cmat[i][j]:
                     ax.plot(
                         [protos[i, 0], protos[j, 0]],