diff --git a/src/mlpack/methods/ann/layer/atrous_convolution_impl.hpp b/src/mlpack/methods/ann/layer/atrous_convolution_impl.hpp
index 519a409760..bac305fa33 100644
--- a/src/mlpack/methods/ann/layer/atrous_convolution_impl.hpp
+++ b/src/mlpack/methods/ann/layer/atrous_convolution_impl.hpp
@@ -215,10 +215,9 @@ void AtrousConvolution<
 
       if (padW != 0 || padH != 0)
       {
-        gTemp.slice(inMap + batchCount * inSize) += output.submat(
-            rotatedFilter.n_rows / 2, rotatedFilter.n_cols / 2,
-            rotatedFilter.n_rows / 2 + gTemp.n_rows - 1,
-            rotatedFilter.n_cols / 2 + gTemp.n_cols - 1);
+        gTemp.slice(inMap + batchCount * inSize) += output.submat(padW, padH,
+            padW + gTemp.n_rows - 1,
+            padH + gTemp.n_cols - 1);
       }
       else
       {
@@ -308,10 +307,9 @@ void AtrousConvolution<
           (gradientTemp.n_rows < output.n_rows &&
           gradientTemp.n_cols < output.n_cols))
       {
-        gradientTemp.slice(outMapIdx) += output.submat(output.n_rows / 2,
-            output.n_cols / 2,
-            output.n_rows / 2 + gradientTemp.n_rows - 1,
-            output.n_cols / 2 + gradientTemp.n_cols - 1);
+        gradientTemp.slice(outMapIdx) += output.submat(padW, padH,
+            padW + gradientTemp.n_rows - 1,
+            padH + gradientTemp.n_cols - 1);
       }
       else
       {
diff --git a/src/mlpack/methods/ann/layer/convolution_impl.hpp b/src/mlpack/methods/ann/layer/convolution_impl.hpp
index 1eba6478a7..e06905b845 100644
--- a/src/mlpack/methods/ann/layer/convolution_impl.hpp
+++ b/src/mlpack/methods/ann/layer/convolution_impl.hpp
@@ -208,10 +208,9 @@ void Convolution<
 
       if (padW != 0 || padH != 0)
       {
-        gTemp.slice(inMap + batchCount * inSize) += output.submat(
-            rotatedFilter.n_rows / 2, rotatedFilter.n_cols / 2,
-            rotatedFilter.n_rows / 2 + gTemp.n_rows - 1,
-            rotatedFilter.n_cols / 2 + gTemp.n_cols - 1);
+        gTemp.slice(inMap + batchCount * inSize) += output.submat(padW, padH,
+            padW + gTemp.n_rows - 1,
+            padH + gTemp.n_cols - 1);
       }
       else
       {
@@ -288,10 +287,9 @@ void Convolution<
           (gradientTemp.n_rows < output.n_rows &&
           gradientTemp.n_cols < output.n_cols))
       {
-        gradientTemp.slice(outMapIdx) += output.submat(output.n_rows / 2,
-            output.n_cols / 2,
-            output.n_rows / 2 + gradientTemp.n_rows - 1,
-            output.n_cols / 2 + gradientTemp.n_cols - 1);
+        gradientTemp.slice(outMapIdx) += output.submat(padW, padH,
+            padW + gradientTemp.n_rows - 1,
+            padH + gradientTemp.n_cols - 1);
       }
       else
       {
diff --git a/src/mlpack/methods/ann/layer/leaky_relu.hpp b/src/mlpack/methods/ann/layer/leaky_relu.hpp
index dfa7af8e92..a5ef7ed6d8 100644
--- a/src/mlpack/methods/ann/layer/leaky_relu.hpp
+++ b/src/mlpack/methods/ann/layer/leaky_relu.hpp
@@ -46,7 +46,7 @@ class LeakyReLU
  public:
   /**
    * Create the LeakyReLU object using the specified parameters.
-   * The non zero gradient can be adjusted by specifying tha parameter
+   * The non zero gradient can be adjusted by specifying the parameter
    * alpha in the range 0 to 1. Default (alpha = 0.03)
    *
    * @param alpha Non zero gradient
diff --git a/src/mlpack/tests/CMakeLists.txt b/src/mlpack/tests/CMakeLists.txt
index bf6dfa75b1..a32e5407d5 100644
--- a/src/mlpack/tests/CMakeLists.txt
+++ b/src/mlpack/tests/CMakeLists.txt
@@ -26,6 +26,7 @@ add_executable(mlpack_test
   cosine_tree_test.cpp
   cv_test.cpp
   dbscan_test.cpp
+  dcgan_test.cpp
   decision_stump_test.cpp
   decision_tree_test.cpp
   det_test.cpp
diff --git a/src/mlpack/tests/dcgan_test.cpp b/src/mlpack/tests/dcgan_test.cpp
new file mode 100644
index 0000000000..1fb94714f3
--- /dev/null
+++ b/src/mlpack/tests/dcgan_test.cpp
@@ -0,0 +1,280 @@
+/**
+ * @file dcgan_network_test.cpp
+ * @author Shikhar Jaiswal
+ *
+ * Tests the DCGAN network.
+ *
+ * mlpack is free software; you may redistribute it and/or modify it under the
+ * terms of the 3-clause BSD license.  You should have received a copy of the
+ * 3-clause BSD license along with mlpack.  If not, see
+ * http://www.opensource.org/licenses/BSD-3-Clause for more information.
+ */
+#include <mlpack/core.hpp>
+
+#include <mlpack/methods/ann/init_rules/gaussian_init.hpp>
+#include <mlpack/methods/ann/loss_functions/cross_entropy_error.hpp>
+#include <mlpack/methods/ann/loss_functions/sigmoid_cross_entropy_error.hpp>
+#include <mlpack/methods/ann/gan.hpp>
+#include <mlpack/methods/ann/ffn.hpp>
+#include <mlpack/methods/ann/layer/layer.hpp>
+#include <mlpack/methods/softmax_regression/softmax_regression.hpp>
+#include <mlpack/core/optimizers/adam/adam.hpp>
+
+#include <boost/test/unit_test.hpp>
+#include "test_tools.hpp"
+
+using namespace mlpack;
+using namespace mlpack::ann;
+using namespace mlpack::math;
+using namespace mlpack::optimization;
+using namespace mlpack::regression;
+using namespace std::placeholders;
+
+BOOST_AUTO_TEST_SUITE(DCGANNetworkTest);
+
+/*
+ * Tests the DCGAN implementation on the MNIST dataset.
+ * It's not viable to train on bigger parameters due to time constraints.
+ * Please refer mlpack/models repository for the tutorial.
+ */
+BOOST_AUTO_TEST_CASE(DCGANMNISTTest)
+{
+  size_t dNumKernels = 32;
+  size_t discriminatorPreTrain = 5;
+  size_t batchSize = 5;
+  size_t noiseDim = 100;
+  size_t generatorUpdateStep = 1;
+  size_t numSamples = 10;
+  double stepSize = 0.0003;
+  double eps = 1e-8;
+  size_t numEpoches = 1;
+  double tolerance = 1e-5;
+  int datasetMaxCols = 10;
+  bool shuffle = true;
+  double multiplier = 10;
+
+  Log::Info << std::boolalpha
+      << " batchSize = " << batchSize << std::endl
+      << " generatorUpdateStep = " << generatorUpdateStep << std::endl
+      << " noiseDim = " << noiseDim << std::endl
+      << " numSamples = " << numSamples << std::endl
+      << " stepSize = " << stepSize << std::endl
+      << " numEpoches = " << numEpoches << std::endl
+      << " tolerance = " << tolerance << std::endl
+      << " shuffle = " << shuffle << std::endl;
+
+  arma::mat trainData;
+  trainData.load("mnist_first250_training_4s_and_9s.arm");
+  Log::Info << arma::size(trainData) << std::endl;
+
+  if (datasetMaxCols > 0)
+    trainData = trainData.cols(0, datasetMaxCols - 1);
+
+  size_t numIterations = trainData.n_cols * numEpoches;
+  numIterations /= batchSize;
+
+  Log::Info << "Dataset loaded (" << trainData.n_rows << ", "
+            << trainData.n_cols << ")" << std::endl;
+  Log::Info << trainData.n_rows << "--------" << trainData.n_cols << std::endl;
+
+  // Create the Discriminator network
+  FFN<SigmoidCrossEntropyError<> > discriminator;
+  discriminator.Add<Convolution<> >(1, dNumKernels, 4, 4, 2, 2, 1, 1, 28, 28);
+  discriminator.Add<LeakyReLU<> >(0.2);
+  discriminator.Add<Convolution<> >(dNumKernels, 2 * dNumKernels, 4, 4, 2, 2,
+      1, 1, 14, 14);
+  discriminator.Add<LeakyReLU<> >(0.2);
+  discriminator.Add<Convolution<> >(2 * dNumKernels, 4 * dNumKernels, 4, 4,
+      2, 2, 1, 1, 7, 7);
+  discriminator.Add<LeakyReLU<> >(0.2);
+  discriminator.Add<Convolution<> >(4 * dNumKernels, 8 * dNumKernels, 4, 4,
+      2, 2, 2, 2, 3, 3);
+  discriminator.Add<LeakyReLU<> >(0.2);
+  discriminator.Add<Convolution<> >(8 * dNumKernels, 1, 4, 4, 1, 1,
+      1, 1, 2, 2);
+  discriminator.Add<SigmoidLayer<> >();
+
+  // Create the Generator network
+  FFN<SigmoidCrossEntropyError<> > generator;
+  generator.Add<TransposedConvolution<> >(noiseDim, 8 * dNumKernels, 2, 2,
+      1, 1, 1, 1, 1, 1);
+  generator.Add<ReLULayer<> >();
+  generator.Add<TransposedConvolution<> >(8 * dNumKernels, 4 * dNumKernels,
+      2, 2, 1, 1, 0, 0, 2, 2);
+  generator.Add<ReLULayer<> >();
+  generator.Add<TransposedConvolution<> >(4 * dNumKernels, 2 * dNumKernels,
+      5, 5, 2, 2, 1, 1, 3, 3);
+  generator.Add<ReLULayer<> >();
+  generator.Add<TransposedConvolution<> >(2 * dNumKernels, dNumKernels, 8, 8,
+      1, 1, 1, 1, 7, 7);
+  generator.Add<ReLULayer<> >();
+  generator.Add<TransposedConvolution<> >(dNumKernels, 1, 15, 15, 1, 1, 1, 1,
+      14, 14);
+  generator.Add<TanHLayer<> >();
+
+  // Create GAN
+  GaussianInitialization gaussian(0, 1);
+  Adam optimizer(stepSize, batchSize, 0.9, 0.999, eps, numIterations,
+      tolerance, shuffle);
+  std::function<double()> noiseFunction = [] () {
+      return math::RandNormal(0, 1);};
+  GAN<FFN<SigmoidCrossEntropyError<> >, GaussianInitialization,
+      std::function<double()> > gan(trainData, generator, discriminator,
+      gaussian, noiseFunction, noiseDim, batchSize, generatorUpdateStep,
+      discriminatorPreTrain, multiplier);
+
+  Log::Info << "Training..." << std::endl;
+  gan.Train(optimizer);
+
+  // Generate samples
+  Log::Info << "Sampling..." << std::endl;
+  arma::mat noise(noiseDim, 1);
+  size_t dim = std::sqrt(trainData.n_rows);
+  arma::mat generatedData(2 * dim, dim * numSamples);
+
+  for (size_t i = 0; i < numSamples; i++)
+  {
+    arma::mat samples;
+    noise.imbue( [&]() { return noiseFunction(); } );
+
+    generator.Forward(noise, samples);
+    samples.reshape(dim, dim);
+    samples = samples.t();
+
+    generatedData.submat(0, i * dim, dim - 1, i * dim + dim - 1) = samples;
+
+    samples = trainData.col(math::RandInt(0, trainData.n_cols));
+    samples.reshape(dim, dim);
+    samples = samples.t();
+
+    generatedData.submat(dim,
+        i * dim, 2 * dim - 1, i * dim + dim - 1) = samples;
+  }
+
+  Log::Info << "Output generated!" << std::endl;
+}
+
+/*
+ * Tests the DCGAN implementation on the CelebA dataset.
+ * It's currently not possible to run this every time due to time constraints.
+ * Please refer mlpack/models repository for the tutorial.
+
+BOOST_AUTO_TEST_CASE(DCGANCelebATest)
+{
+  size_t dNumKernels = 64;
+  size_t discriminatorPreTrain = 300;
+  size_t batchSize = 1;
+  size_t noiseDim = 100;
+  size_t generatorUpdateStep = 1;
+  size_t numSamples = 10;
+  double stepSize = 0.0003;
+  double eps = 1e-8;
+  size_t numEpoches = 20;
+  double tolerance = 1e-5;
+  int datasetMaxCols = -1;
+  bool shuffle = true;
+  double multiplier = 10;
+
+  Log::Info << std::boolalpha
+      << " batchSize = " << batchSize << std::endl
+      << " generatorUpdateStep = " << generatorUpdateStep << std::endl
+      << " noiseDim = " << noiseDim << std::endl
+      << " numSamples = " << numSamples << std::endl
+      << " stepSize = " << stepSize << std::endl
+      << " numEpoches = " << numEpoches << std::endl
+      << " tolerance = " << tolerance << std::endl
+      << " shuffle = " << shuffle << std::endl;
+
+  arma::mat trainData;
+  trainData.load("celeba.csv");
+  Log::Info << arma::size(trainData) << std::endl;
+
+  if (datasetMaxCols > 0)
+    trainData = trainData.cols(0, datasetMaxCols - 1);
+
+  size_t numIterations = trainData.n_cols * numEpoches;
+  numIterations /= batchSize;
+
+  Log::Info << "Dataset loaded (" << trainData.n_rows << ", "
+            << trainData.n_cols << ")" << std::endl;
+  Log::Info << trainData.n_rows << "--------" << trainData.n_cols << std::endl;
+
+  // Create the Discriminator network
+  FFN<SigmoidCrossEntropyError<> > discriminator;
+  discriminator.Add<Convolution<> >(3, dNumKernels, 4, 4, 2, 2, 1, 1, 64, 64);
+  discriminator.Add<LeakyReLU<> >(0.2);
+  discriminator.Add<Convolution<> >(dNumKernels, 2 * dNumKernels, 4, 4, 2, 2,
+      1, 1, 32, 32);
+  discriminator.Add<LeakyReLU<> >(0.2);
+  discriminator.Add<Convolution<> >(2 * dNumKernels, 4 * dNumKernels, 4, 4,
+      2, 2, 1, 1, 16, 16);
+  discriminator.Add<LeakyReLU<> >(0.2);
+  discriminator.Add<Convolution<> >(4 * dNumKernels, 8 * dNumKernels, 4, 4,
+      2, 2, 1, 1, 8, 8);
+  discriminator.Add<LeakyReLU<> >(0.2);
+  discriminator.Add<Convolution<> >(8 * dNumKernels, 1, 4, 4, 1, 1,
+      0, 0, 4, 4);
+  discriminator.Add<SigmoidLayer<> >();
+
+  // Create the Generator network
+  FFN<SigmoidCrossEntropyError<> > generator;
+  generator.Add<TransposedConvolution<> >(noiseDim, 8 * dNumKernels, 4, 4,
+      1, 1, 2, 2, 1, 1);
+  generator.Add<ReLULayer<> >();
+  generator.Add<TransposedConvolution<> >(8 * dNumKernels, 4 * dNumKernels,
+      5, 5, 1, 1, 1, 1, 4, 4);
+  generator.Add<ReLULayer<> >();
+  generator.Add<TransposedConvolution<> >(4 * dNumKernels, 2 * dNumKernels,
+      9, 9, 1, 1, 1, 1, 8, 8);
+  generator.Add<ReLULayer<> >();
+  generator.Add<TransposedConvolution<> >(2 * dNumKernels, dNumKernels, 17, 17,
+      1, 1, 1, 1, 16, 16);
+  generator.Add<ReLULayer<> >();
+  generator.Add<TransposedConvolution<> >(dNumKernels, 3, 33, 33, 1, 1, 1, 1,
+      32, 32);
+  generator.Add<TanHLayer<> >();
+
+  // Create GAN
+  GaussianInitialization gaussian(0, 1);
+  Adam optimizer(stepSize, batchSize, 0.9, 0.999, eps, numIterations,
+      tolerance, shuffle);
+  std::function<double()> noiseFunction = [] () {
+      return math::RandNormal(0, 1);};
+  GAN<FFN<SigmoidCrossEntropyError<> >, GaussianInitialization,
+      std::function<double()> > gan(trainData, generator, discriminator,
+      gaussian, noiseFunction, noiseDim, batchSize, generatorUpdateStep,
+      discriminatorPreTrain, multiplier);
+
+  Log::Info << "Training..." << std::endl;
+  gan.Train(optimizer);
+
+  // Generate samples
+  Log::Info << "Sampling..." << std::endl;
+  arma::mat noise(noiseDim, 1);
+  size_t dim = std::sqrt(trainData.n_rows);
+  arma::mat generatedData(2 * dim, dim * numSamples);
+
+  for (size_t i = 0; i < numSamples; i++)
+  {
+    arma::mat samples;
+    noise.imbue( [&]() { return noiseFunction(); } );
+
+    generator.Forward(noise, samples);
+    samples.reshape(dim, dim);
+    samples = samples.t();
+
+    generatedData.submat(0, i * dim, dim - 1, i * dim + dim - 1) = samples;
+
+    samples = trainData.col(math::RandInt(0, trainData.n_cols));
+    samples.reshape(dim, dim);
+    samples = samples.t();
+
+    generatedData.submat(dim,
+        i * dim, 2 * dim - 1, i * dim + dim - 1) = samples;
+  }
+
+  Log::Info << "Output generated!" << std::endl;
+}
+*/
+
+BOOST_AUTO_TEST_SUITE_END();
diff --git a/src/mlpack/tests/gan_test.cpp b/src/mlpack/tests/gan_test.cpp
index 40fc4c12f2..a6d47c6f9e 100644
--- a/src/mlpack/tests/gan_test.cpp
+++ b/src/mlpack/tests/gan_test.cpp
@@ -132,28 +132,26 @@ BOOST_AUTO_TEST_CASE(GANTest)
 }
 
 /*
- * Tests the GAN implementation on the O'Reilly Test on the MNIST dataset.
- * It's currently not possible to run this every time due to time constraints.
+ * Tests the GAN implementation of the O'Reilly Test on the MNIST dataset.
+ * It's not viable to train on bigger parameters due to time constraints.
  * Please refer mlpack/models repository for the tutorial.
-
+ */
 BOOST_AUTO_TEST_CASE(GANMNISTTest)
 {
   size_t dNumKernels = 32;
-  size_t discriminatorPreTrain = 300;
-  size_t batchSize = 50;
+  size_t discriminatorPreTrain = 5;
+  size_t batchSize = 5;
   size_t noiseDim = 100;
   size_t generatorUpdateStep = 1;
   size_t numSamples = 10;
   double stepSize = 0.0003;
   double eps = 1e-8;
-  size_t numEpoches = 20;
+  size_t numEpoches = 1;
   double tolerance = 1e-5;
-  int datasetMaxCols = -1;
+  int datasetMaxCols = 10;
   bool shuffle = true;
   double multiplier = 10;
-  std::string output_dataset = "output_mnist.csv";
 
-  Log::Info << "output_dataset = '" << output_dataset << "'" << std::endl;
   Log::Info << std::boolalpha
       << " batchSize = " << batchSize << std::endl
       << " generatorUpdateStep = " << generatorUpdateStep << std::endl
@@ -244,10 +242,7 @@ BOOST_AUTO_TEST_CASE(GANMNISTTest)
         i * dim, 2 * dim - 1, i * dim + dim - 1) = samples;
   }
 
-  Log::Info << "Saving output to " << output_dataset << "..." << std::endl;
-  generatedData.save(output_dataset, arma::csv_ascii);
-  Log::Info << "Output saved!" << std::endl;
+  Log::Info << "Output generated!" << std::endl;
 }
-*/
 
 BOOST_AUTO_TEST_SUITE_END();