Active Learning for Artificial Neural Network Models
Publicerad
Författare
Typ
Examensarbete för masterexamen
Program
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Active learning is the field of choosing informative data to train machine learning
models. This thesis covers eight separate substudies investigating how to maximize
the test accuracy for deep feed-forward artificial neural network models using active
learning. When performing active learning, a query strategy must be specified which
is why four different query strategies were examined, namely the margin, entropy,
least confident, and one suggested by the authors, the least squares. The data sets
MNIST, Fashion-MNIST, and CIFAR-10 were used to see how the results generalize
between data sets. With the eight substudies examined, we have concluded some
suggestions that should be considered to improve the test accuracy:
(1) Among the query strategies examined, the margin query strategy consistently
selected data which gave rise to the highest test accuracy. (2) The cumulative
training method is most suitable to train feed-forward neural networks when using
a query strategy. This means that the networks should be reset and retrained using
all labeled data. (3) For improved performance, a query strategy should be used after
the network has trained on some initially randomly selected data. (4) If the mean
margin informativeness measure, used internally by the margin query strategies,
starts to decrease during training, then one should consider gathering more unlabeled
data or stop labeling to reduce cost. (5) The semi-supervised pseudo-label algorithm
may be used to further increase test accuracy by utilizing the unlabeled data set. (6)
To estimate the performance of a network without the presence of a dedicated labeled
test set, one can use the randomly sampled data from (3) to create an upper and
lower estimate of the test accuracy. We have shown, through empirical studies, that
steps (1)-(6) are all associated with some benefit when performing active learning.
Beskrivning
Ämne/nyckelord
Active Learning, Machine Learning, Artificial Neural Networks, Sampling technique