24 research outputs found
Semantic knowledge integration for learning from semantically imprecise data
Low availability of labeled training data often poses a fundamental limit to the accuracy of computer vision applications using machine learning methods. While these methods are improved continuously, e.g., through better neural network architectures, there cannot be a single methodical change that increases the accuracy on all possible tasks. This statement, known as the no free lunch theorem, suggests that we should consider aspects of machine learning other than learning algorithms for opportunities to escape the limits set by the available training data. In this thesis, we focus on two main aspects, namely the nature of the training data, where we introduce structure into the label set using concept hierarchies, and the learning paradigm, which we change in accordance with requirements of real-world applications as opposed to more academic setups.Concept hierarchies represent semantic relations, which are sets of statements such as "a bird is an animal." We propose a hierarchical classifier to integrate this domain knowledge in a pre-existing task, thereby increasing the information the classifier has access to. While the hierarchy's leaf nodes correspond to the original set of classes, the inner nodes are "new" concepts that do not exist in the original training data. However, we pose that such "imprecise" labels are valuable and should occur naturally, e.g., as an annotator's way of expressing their uncertainty. Furthermore, the increased number of concepts leads to more possible search terms when assembling a web-crawled dataset or using an image search. We propose CHILLAX, a method that learns from semantically imprecise training data, while still offering precise predictions to integrate seamlessly into a pre-existing application
Integrating domain knowledge: using hierarchies to improve deep classifiers
One of the most prominent problems in machine learning in the age of deep
learning is the availability of sufficiently large annotated datasets. While
for standard problem domains (ImageNet classification), appropriate datasets
exist, for specific domains, \eg classification of animal species, a long-tail
distribution means that some classes are observed and annotated insufficiently.
Challenges like iNaturalist show that there is a strong interest in species
recognition. Acquiring additional labels can be prohibitively expensive. First,
since domain experts need to be involved, and second, because acquisition of
new data might be costly. Although there exist methods for data augmentation,
which not always lead to better performance of the classifier, there is more
additional information available that is to the best of our knowledge not
exploited accordingly.
In this paper, we propose to make use of existing class hierarchies like
WordNet to integrate additional domain knowledge into classification. We encode
the properties of such a class hierarchy into a probabilistic model. From
there, we derive a special label encoding together with a corresponding loss
function. Using a convolutional neural network, on the ImageNet and NABirds
datasets our method offers a relative improvement of 10.4% and 9.6% in accuracy
over the baseline respectively. After less than a third of training time, it is
already able to match the baseline's fine-grained recognition performance. Both
results show that our suggested method is efficient and effective.Comment: 9 pages, 7 figure
Convolutional Patch Networks with Spatial Prior for Road Detection and Urban Scene Understanding
Classifying single image patches is important in many different applications,
such as road detection or scene understanding. In this paper, we present
convolutional patch networks, which are convolutional networks learned to
distinguish different image patches and which can be used for pixel-wise
labeling. We also show how to incorporate spatial information of the patch as
an input to the network, which allows for learning spatial priors for certain
categories jointly with an appearance model. In particular, we focus on road
detection and urban scene understanding, two application areas where we are
able to achieve state-of-the-art results on the KITTI as well as on the
LabelMeFacade dataset.
Furthermore, our paper offers a guideline for people working in the area and
desperately wandering through all the painstaking details that render training
CNs on image patches extremely difficult.Comment: VISAPP 2015 pape
ROMEO: Exploring Juliet through the Lens of Assembly Language
Automatic vulnerability detection on C/C++ source code has benefitted from
the introduction of machine learning to the field, with many recent
publications considering this combination. In contrast, assembly language or
machine code artifacts receive little attention, although there are compelling
reasons to study them. They are more representative of what is executed, more
easily incorporated in dynamic analysis and in the case of closed-source code,
there is no alternative. We propose ROMEO, a publicly available, reproducible
and reusable binary vulnerability detection benchmark dataset derived from the
Juliet test suite. Alongside, we introduce a simple text-based assembly
language representation that includes context for function-spanning
vulnerability detection and semantics to detect high-level vulnerabilities.
Finally, we show that this representation, combined with an off-the-shelf
classifier, compares favorably to state-of-the-art methods, including those
operating on the full C/C++ code.Comment: 6 pages, code available at https://gitlab.com/dlr-dw/rome