\section{A. Filter Intensities and their Respective Parameters}
In Section 4.1 we introduce abstract levels of intensities for each filter we apply to an image. We now map each intensity to actual parameters passed to editing libraries to achieve the given filter intensity.

\begin{table}[h]
\centering
\begin{tabular}{lll}
\hline
Distortion & Intensity & Actual Parameters \\
\hline
\multicolumn{3}{l}{\textit{Technical}} \\
JPEG compression & [0, ..., +4, +5] & \\
Defocus blur & [0, ..., +4, +5] & \\
Motion blur & [0, ..., +4, +5] & \\
Pixelate & [0, ..., +4, +5] & \\
Gaussian noise & [0, ..., +4, +5] & \\
Impulse noise & [0, ..., +4, +5] & The editing library accepts the technical intensities as is. \\
\hline
\multicolumn{3}{l}{\textit{Style}} \\
Brightness & [-5, -4, ..., +4, +5] & [-1.0, -0.8, ..., 0.8, 1.0] \\
Contrast & [-5, -4, ..., +4, +5] & [-1.0, -0.8, ..., 0.8, 1.0] \\
Saturation & [-5, -4, ..., +4, +5] & [-1.0, -0.8, ..., 0.8, 1.0] \\
Exposure & [-5, -4, ..., +4, +5] & [-3.0, -2.4, ..., 2.4, 3.0] \\
Shadows & [-5, -4, ..., +2, +3] & [-100, -60, -20, 20, 40, 50, 60, 80, 100] \\
Highlights & [-3, -2, ..., +4, +5] & [-100, -80, -60, -50, -40, -20, 20, 60, 100] \\
Temperature & [-4, -3, ..., +4, +5] & [2000, 3000, 5000, 6000, 6500, 7000, 8000, 10000, 14000, 18000] \\
Tint & [-5, -4, ..., +4, +5] & [0.75, 0.8, ..., 1.2, 1.25] \\
Vibrance & [-2, -1, ..., +3, +4] & [0, 20, 25, 40, 60, 80, 100] \\
\hline
\multicolumn{3}{l}{\textit{Composition}} \\
Rotation & [-5, -4, ..., +4, +5] & [10$^{\circ}$, 8$^{\circ}$, ..., 8$^{\circ}$, 10$^{\circ}$] \\
Horizontal crop & [-5, -4, ..., +4, +5] & \\
Vertical crop & [-5, -4, ..., +4, +5] & \\
Left Diagonal crop & [-5, -4, ..., +4, +5] & We resize the image to 336px and then crop patches of size 224px from the resulting image. Intensity 0 is a centercrop, while a $|intensity| == 5$ results in a crop from the images' border. \\
Image Ratio & [-5, -4, ..., +4, +5] & [stretch along y-axis 100\%, x80\%, ..., x80\%, stretch along x-axis 100\%] \\
\hline
\end{tabular}
\caption{Actual parameters and implementation specifics for each distortion and intensity level. Technical parameters are passed to imagenet-c \cite{12} and style parameters to darktable \cite{37} while compositional distortions are implemented by us.}
\label{table_3}
\end{table}

\section{B. Dataset Content Analysis}
To show that the images of our dataset (Section 4.2) contain a large variety of contents, we apply a pretrained DenseNet121 \cite{16} for image classification and RetinaNet \cite{25} for object detection on our newly introduced dataset. We find that the images of our dataset spread across many different classes and contain a wide variety of objects and subjects.

\begin{table}[h]
\centering
\begin{tabular}{lrlr}
\hline
\multicolumn{2}{c}{most common classes} & \multicolumn{2}{c}{most common objects} \\
class & count & object & count \\
\hline
seashore & 3554 & person & 44301 \\
alp & 2568 & car & 3186 \\
lakeside & 2446 & cup & 2880 \\
fountain & 2265 & bird & 2788 \\
valley & 2011 & cell phone & 1749 \\
miniskirt & 1455 & boat & 1618 \\
gown & 1430 & dog & 1581 \\
bikini & 1176 & potted plant & 1580 \\
... & ... & ... & ... \\
sloth\_bear & 2 & refrigerator & 31 \\
affenpinscher & 2 & snowboard & 25 \\
patas & 1 & skis & 23 \\
Sealyham\_terrier & 1 & hair dryer & 7 \\
Japanese\_spaniel & 1 & toaster & 7 \\
\hline
\end{tabular}
\caption{Most commonly detected classes and objects in the images of our dataset. Full list: https://github.com/janpf/self-supervised-multi-task-aesthetic-pretraining}
\label{table_4}
\end{table}