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@@ -77,7 +77,7 @@
Introduction
$$
where:
- - $ \mathbf{p} $: Input 2D pixel position, normalized to $0, 1]\$or $-1, 1]$.
+ - $ \mathbf{p} $: Input 2D pixel position.
- $ \mathbf{c} $: Output color at that position.
- $ f_\theta $: Neural network parameterized by weights $\theta $
@@ -242,6 +242,73 @@ Fox
+
+ Cat
+ I also choose the image from the cute cat Hujiao as input for training the Mlp.
+
+
+
+
+
+ Cat Original Image
+ |
+
+
+
+
+
+ I train my model with $L=10$ and hidden dimension of $512$ for $5000$ iterations and batch size of $5000$. My
+ learning rate is $1e-3$. The model reach a PSNR of $26.33$.
+
+
+
+
+
+
+
+ MSE Loss
+ |
+
+
+ PSNR
+ |
+
+
+
+
+
Hyperparameter Tunning
My first try for hyperparameter tunning is changing the hidden layers to $512$. It slightly increase the PSNR to
$27.48$, which means a higher quality in reconstructing the image.