Random Forests (RFs) have become commonplace
in many computer vision applications. Their
popularity is mainly driven by their high computational
efficiency during both training and evaluation
while still being able to achieve state-of-the-art accuracy.
This work extends the usage of Random Forests to
Semi-Supervised Learning (SSL) problems. We show
that traditional decision trees are optimizing multiclass
margin maximizing loss functions. From this
intuition, we develop a novel multi-class margin definition
for the unlabeled data, and an iterative deterministic
annealing-style training algorithm maximizing
both the multi-class margin of labeled and unlabeled
samples. In particular, this allows us to use
the predicted labels of the unlabeled data as additional
optimization variables. Furthermore, we propose
a control mechanism based on the out-of-bag
error, which prevents the algorithm from degradation
if the unlabeled data is not useful for the task.
Our experiments ...