diff --git a/README.md b/README.md
index 6aaa6646..bf7ed03a 100644
--- a/README.md
+++ b/README.md
@@ -32,7 +32,7 @@ Collection of PyTorch implementations of Generative Adversarial Network varietie
     + [Softmax GAN](#softmax-gan)
     + [StarGAN](#stargan)
     + [Super-Resolution GAN](#super-resolution-gan)
-    + [UNIT](#UNIT)
+    + [UNIT](#unit)
     + [Wasserstein GAN](#wasserstein-gan)
     + [Wasserstein GAN GP](#wasserstein-gan-gp)
 
diff --git a/implementations/pix2pix/models.py b/implementations/pix2pix/models.py
index 7788cfcf..7df5547b 100644
--- a/implementations/pix2pix/models.py
+++ b/implementations/pix2pix/models.py
@@ -52,11 +52,11 @@ def __init__(self, in_channels=3, out_channels=3):
         self.down1 = UNetDown(in_channels, 64, normalize=False)
         self.down2 = UNetDown(64, 128)
         self.down3 = UNetDown(128, 256)
-        self.down4 = UNetDown(256, 512)
-        self.down5 = UNetDown(512, 512)
-        self.down6 = UNetDown(512, 512)
-        self.down7 = UNetDown(512, 512)
-        self.down8 = UNetDown(512, 512, normalize=False)
+        self.down4 = UNetDown(256, 512, dropout=0.5)
+        self.down5 = UNetDown(512, 512, dropout=0.5)
+        self.down6 = UNetDown(512, 512, dropout=0.5)
+        self.down7 = UNetDown(512, 512, dropout=0.5)
+        self.down8 = UNetDown(512, 512, normalize=False, dropout=0.5)
 
         self.up1 = UNetUp(512, 512, dropout=0.5)
         self.up2 = UNetUp(1024, 512, dropout=0.5)
diff --git a/implementations/pix2pix/pix2pix.py b/implementations/pix2pix/pix2pix.py
index a8ed7c6a..fe5f79a8 100644
--- a/implementations/pix2pix/pix2pix.py
+++ b/implementations/pix2pix/pix2pix.py
@@ -104,7 +104,7 @@ def sample_images(batches_done):
 #  Training
 # ----------
 
-start_time = time.time()
+prev_time = time.time()
 
 for epoch in range(opt.epoch, opt.n_epochs):
     for i, batch in enumerate(dataloader):
@@ -164,7 +164,8 @@ def sample_images(batches_done):
         # Determine approximate time left
         batches_done = epoch * len(dataloader) + i
         batches_left = opt.n_epochs * len(dataloader) - batches_done
-        time_left = datetime.timedelta(seconds=batches_left * (time.time() - start_time)/ (batches_done + 1))
+        time_left = datetime.timedelta(seconds=batches_left * (time.time() - prev_time))
+        prev_time = time.time()
 
         # Print log
         sys.stdout.write("\r[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f, pixel: %f, adv: %f] ETA: %s" %
diff --git a/requirements.txt b/requirements.txt
index ad423af4..91545b78 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,10 +1,8 @@
-torch
+torch>=0.4.0
 torchvision
 matplotlib
 numpy
 scipy
 pillow
-urllib
-skimage
-gzip
-pickle
+urllib3
+scikit-image