diff --git a/model/set_encoders.py b/model/set_encoders.py
index d9ad189..f92afc5 100644
--- a/model/set_encoders.py
+++ b/model/set_encoders.py
@@ -30,33 +30,22 @@ SOFTWARE.
 
 import torch
 import torch.nn as nn
-import torch.nn.functional as F
-
-from model.feature_extractors import create_feature_extractor
 
 class SetEncoder(nn.Module):
     """
     Simple set encoder implementing DeepSets (https://arxiv.org/abs/1703.06114). Used for modeling permutation-invariant representations on sets (mainly for extracting task-level embedding of context sets).
     """
-    def __init__(self, encoder_name='efficientnet_b0', task_embedding_dim=1280):
+    def __init__(self):
         """
         Creates an instance of SetEncoder.
         :return: Nothing.
         """
         super(SetEncoder, self).__init__()
-        self.task_embedding_dim = task_embedding_dim
-        self.encoder_name = encoder_name
-
-        self.encoder, _ = create_feature_extractor(
-                                    feature_extractor_name=self.encoder_name, 
-                                    pretrained=True,
-                                    with_film=False,
-                                    learn_extractor=False
-                                    )
- 
+        self.encoder = SimplePrePoolNet()
+    
     def forward(self, x):
         """
-        Function that encodes a set of N elements into N embeddings, each of dim self.task_embedding_dim
+        Function that encodes a set of N elements into N embeddings, each of dim 64
         :param x: (torch.Tensor) Set of elements (for clips it has the shape: batch x clip length x C x H x W).
         :return: (torch.Tensor) Individual element embeddings.
         """
@@ -87,8 +76,49 @@ class SetEncoder(nn.Module):
 
     @property
     def output_size(self):
-        return self.task_embedding_dim
+        return 64
 
+class SimplePrePoolNet(nn.Module):
+    """
+    Simple network to encode elements of a set into low-dimensional embeddings. Used before pooling them to obtain a task-level embedding. A multi-layer convolutional network is used, similar to that in https://github.com/cambridge-mlg/cnaps.
+    """
+    def __init__(self):
+        """
+        Creates an instance of SimplePrePoolNet.
+        :return: Nothing.
+        """
+        super(SimplePrePoolNet, self).__init__()
+        self.layer1 = self._make_conv2d_layer(3, 64)
+        self.layer2 = self._make_conv2d_layer(64, 64)
+        self.layer3 = self._make_conv2d_layer(64, 64)
+        self.layer4 = self._make_conv2d_layer(64, 64)
+        self.layer5 = self._make_conv2d_layer(64, 64)
+        self.avgpool = nn.AdaptiveAvgPool2d((1,1))
+
+    @staticmethod
+    def _make_conv2d_layer(in_maps, out_maps, kernel_size=3):
+        return nn.Sequential(
+            nn.Conv2d(in_maps, out_maps, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(out_maps),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=False)
+        )
+
+    def forward(self, x):
+        """
+        Function that encodes each element in x into a 64-dimensional embedding.
+        :param x: (torch.Tensor) Set of elements (for clips it has the shape: batch*clip length x C x H x W).
+        :return: (torch.Tensor) Each element in x encoded as a 64-dimensional vector.
+        """
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+        x = self.layer5(x)
+        x = self.avgpool(x)
+        x = x.view(x.size(0), -1)
+        return x
+    
 class NullSetEncoder(nn.Module):
     def __init__(self):
         super().__init__()