diff --git a/body.tex b/body.tex
index 4bab418..a1a04bc 100644
--- a/body.tex
+++ b/body.tex
@@ -492,7 +492,13 @@ has been modified to work inside a Python package architecture and
 with eager mode. It is stored as a Git submodule inside the package
 repository.
 
-\section{Preparation of data sets}
+\chapter{Experimental Setup and Results}
+
+\label{chap:experiments-results}
+
+\section{Data sets}
+
+% TODO: reword
 
 Usually, data sets are not perfect when it comes to neural
 networks: they contain outliers, invalid bounding boxes, and similar
@@ -539,6 +545,8 @@ all trajectories: a list of lists of lists.
 
 \section{Replication of Miller et al.}
 
+% TODO rework
+
 Miller et al. use SSD for the object detection part. They compare
 vanilla SSD, vanilla SSD with entropy thresholding, and the
 Bayesian SSD with each other. The Bayesian SSD was created by
@@ -571,32 +579,6 @@ between trajectories: some classes are only present in some
 trajectories. This makes training with SSD on SceneNet practically
 impossible.
 
-\section{Implementing an auto-encoder}
-
-Pidhorskyi et al.~\cite{Pidhorskyi2018} released their source code
-but it is for
-PyTorch; I had to adapt the code for Tensorflow. For the proof of
-concept, a simpler model of encoder and generator was used; the
-adversarial parts were disabled for this. The encoder starts with
-a sigmoid-activated convolutional layers, followed by two
-convolutional layers with ReLU as activation function. It ends
-with a Flatten and Dense layer.
-Decoding starts with a Dense layer, followed by three transposed
-convolutional layers with ReLU as activation function; the last
-layer is a transposed convolutional layer with sigmoid as
-activation function.
-
-The auto-encoder works on the MNIST data set, as expected. It
-works very well for COCO as well, with one caveat: it is equally
-good for all classes, even when trained only on one. Novelty
-detection is out of the question under theses circumstances.
-
-\chapter{Experimental Setup and Results}
-
-\label{chap:experiments-results}
-
-\section{Data sets}
-
 \section{Experimental Setup}
 
 \section{Results}