diff --git a/src/eynollah/training/models.py b/src/eynollah/training/models.py
index 13a35a1..a95ba7e 100644
--- a/src/eynollah/training/models.py
+++ b/src/eynollah/training/models.py
@@ -212,7 +212,7 @@ def unet_decoder(img, f1, f2, f3, f4, f5, n_classes, light=False, task="segmenta
         f4 = BatchNormalization(axis=bn_axis)(f4)
         f4 = Activation('relu')(f4)
 
-    o = UpSampling2D((2, 2), data_format=IMAGE_ORDERING)(o)
+    o = UpSampling2D((2, 2), data_format=IMAGE_ORDERING, interpolation="bilinear")(o)
     o = concatenate([o, f4], axis=MERGE_AXIS)
     o = ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING)(o)
     o = Conv2D(512, (3, 3), padding='valid',
@@ -220,7 +220,7 @@ def unet_decoder(img, f1, f2, f3, f4, f5, n_classes, light=False, task="segmenta
     o = BatchNormalization(axis=bn_axis)(o)
     o = Activation('relu')(o)
 
-    o = UpSampling2D((2, 2), data_format=IMAGE_ORDERING)(o)
+    o = UpSampling2D((2, 2), data_format=IMAGE_ORDERING, interpolation="bilinear")(o)
     o = concatenate([o, f3], axis=MERGE_AXIS)
     o = ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING)(o)
     o = Conv2D(256, (3, 3), padding='valid',
@@ -228,7 +228,7 @@ def unet_decoder(img, f1, f2, f3, f4, f5, n_classes, light=False, task="segmenta
     o = BatchNormalization(axis=bn_axis)(o)
     o = Activation('relu')(o)
 
-    o = UpSampling2D((2, 2), data_format=IMAGE_ORDERING)(o)
+    o = UpSampling2D((2, 2), data_format=IMAGE_ORDERING, interpolation="bilinear")(o)
     o = concatenate([o, f2], axis=MERGE_AXIS)
     o = ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING)(o)
     o = Conv2D(128, (3, 3), padding='valid',
@@ -236,7 +236,7 @@ def unet_decoder(img, f1, f2, f3, f4, f5, n_classes, light=False, task="segmenta
     o = BatchNormalization(axis=bn_axis)(o)
     o = Activation('relu')(o)
 
-    o = UpSampling2D((2, 2), data_format=IMAGE_ORDERING)(o)
+    o = UpSampling2D((2, 2), data_format=IMAGE_ORDERING, interpolation="bilinear")(o)
     o = concatenate([o, f1], axis=MERGE_AXIS)
     o = ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING)(o)
     o = Conv2D(64, (3, 3), padding='valid',
@@ -244,7 +244,7 @@ def unet_decoder(img, f1, f2, f3, f4, f5, n_classes, light=False, task="segmenta
     o = BatchNormalization(axis=bn_axis)(o)
     o = Activation('relu')(o)
 
-    o = UpSampling2D((2, 2), data_format=IMAGE_ORDERING)(o)
+    o = UpSampling2D((2, 2), data_format=IMAGE_ORDERING, interpolation="bilinear")(o)
     o = concatenate([o, img], axis=MERGE_AXIS)
     o = ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING)(o)
     o = Conv2D(32, (3, 3), padding='valid',