From 5c508a4d686890a2a94e4df529a3874ff23102ac Mon Sep 17 00:00:00 2001
From: Rea Fernandes <zob06qih@rhrk.uni-kl.de>
Date: Sun, 26 Nov 2023 21:29:44 +0100
Subject: [PATCH] Solution 2.1.3

---
 exercises/exercise2/exercise2.tex | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)

diff --git a/exercises/exercise2/exercise2.tex b/exercises/exercise2/exercise2.tex
index 0063188..8863c49 100644
--- a/exercises/exercise2/exercise2.tex
+++ b/exercises/exercise2/exercise2.tex
@@ -79,6 +79,25 @@ We adopt the convention that for a 2D array $X$ (or $K$), $X_{i,j}$ denotes the
 
 \subsection{What is the difference between 'valid' and 'same' padding?}
 
+Solution
+
+\textbf{VALID Padding:}
+\begin{itemize}
+    \item No padding is added to the input image.
+    \item The filter window always stays inside the input image.
+    \item Loss of information may occur, especially on the right and bottom edges.
+    \item Output size is generally smaller than the input size.
+\end{itemize}
+
+\textbf{SAME Padding:}
+\begin{itemize}
+    \item Input is half padded to ensure the filter is applied to all input elements.
+    \item Output size is the same as the input size when the stride is 1.
+    \item "SAME" is commonly used during model training for computational convenience.
+    \item Output size calculation: $output\_spatial\_shape[i] = \lceil \frac{input\_spatial\_shape[i]}{strides[i]} \rceil$
+\end{itemize}
+
+
 \subsection{Why do we prefer Convolutional Neural Networks (CNNs) over a Multi-Layer Perceptron (MLP) for image data?}
 
 In contrast to CNN, which uses a tensor as input, Multi-Layer Perceptron uses a vector. Consequently, CNN has a superior understanding of the spatial relation—the relationship between adjacent pixels in a picture. Hence, CNN will function better for complex images. 
-- 
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