  Training deep neural networks is challenging when large and annotated
datasets are unavailable. Extensive manual annotation of data samples is
time-consuming, expensive, and error-prone, notably when it needs to be done by
experts. To address this issue, increased attention has been devoted to
techniques that propagate uncertain labels (also called pseudo labels) to large
amounts of unsupervised samples and use them for training the model. However,
these techniques still need hundreds of supervised samples per class in the
training set and a validation set with extra supervised samples to tune the
model. We improve a recent iterative pseudo-labeling technique, Deep Feature
Annotation (DeepFA), by selecting the most confident unsupervised samples to
iteratively train a deep neural network. Our confidence-based sampling strategy
relies on only dozens of annotated training samples per class with no
validation set, considerably reducing user effort in data annotation. We first
ascertain the best configuration for the baseline -- a self-trained deep neural
network -- and then evaluate our confidence DeepFA for different confidence
thresholds. Experiments on six datasets show that DeepFA already outperforms
the self-trained baseline, but confidence DeepFA can considerably outperform
the original DeepFA and the baseline.
