22,130 research outputs found
Artificial Intelligence : from Research to Application ; the Upper-Rhine Artificial Intelligence Symposium (UR-AI 2019)
The TriRhenaTech alliance universities and their partners presented their
competences in the field of artificial intelligence and their cross-border
cooperations with the industry at the tri-national conference 'Artificial
Intelligence : from Research to Application' on March 13th, 2019 in Offenburg.
The TriRhenaTech alliance is a network of universities in the Upper Rhine
Trinational Metropolitan Region comprising of the German universities of
applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, and Offenburg, the
Baden-Wuerttemberg Cooperative State University Loerrach, the French university
network Alsace Tech (comprised of 14 'grandes \'ecoles' in the fields of
engineering, architecture and management) and the University of Applied
Sciences and Arts Northwestern Switzerland. The alliance's common goal is to
reinforce the transfer of knowledge, research, and technology, as well as the
cross-border mobility of students
Efficient Coarse-to-Fine Non-Local Module for the Detection of Small Objects
An image is not just a collection of objects, but rather a graph where each
object is related to other objects through spatial and semantic relations.
Using relational reasoning modules, such as the non-local module
\cite{wang2017non}, can therefore improve object detection. Current schemes
apply such dedicated modules either to a specific layer of the bottom-up
stream, or between already-detected objects. We show that the relational
process can be better modeled in a coarse-to-fine manner and present a novel
framework, applying a non-local module sequentially to increasing resolution
feature maps along the top-down stream. In this way, information can naturally
passed from larger objects to smaller related ones. Applying the module to fine
feature maps further allows the information to pass between the small objects
themselves, exploiting repetitions of instances of the same class. In practice,
due to the expensive memory utilization of the non-local module, it is
infeasible to apply the module as currently used to high-resolution feature
maps. We redesigned the non local module, improved it in terms of memory and
number of operations, allowing it to be placed anywhere along the network. We
further incorporated relative spatial information into the module, in a manner
that can be incorporated into our efficient implementation. We show the
effectiveness of our scheme by improving the results of detecting small objects
on COCO by 1-2 AP points over Faster and Mask RCNN and by 1 AP over using
non-local module on the bottom-up stream
Answering Visual-Relational Queries in Web-Extracted Knowledge Graphs
A visual-relational knowledge graph (KG) is a multi-relational graph whose
entities are associated with images. We explore novel machine learning
approaches for answering visual-relational queries in web-extracted knowledge
graphs. To this end, we have created ImageGraph, a KG with 1,330 relation
types, 14,870 entities, and 829,931 images crawled from the web. With
visual-relational KGs such as ImageGraph one can introduce novel probabilistic
query types in which images are treated as first-class citizens. Both the
prediction of relations between unseen images as well as multi-relational image
retrieval can be expressed with specific families of visual-relational queries.
We introduce novel combinations of convolutional networks and knowledge graph
embedding methods to answer such queries. We also explore a zero-shot learning
scenario where an image of an entirely new entity is linked with multiple
relations to entities of an existing KG. The resulting multi-relational
grounding of unseen entity images into a knowledge graph serves as a semantic
entity representation. We conduct experiments to demonstrate that the proposed
methods can answer these visual-relational queries efficiently and accurately
Hide-and-Seek: A Template for Explainable AI
Lack of transparency has been the Achilles heal of Neural Networks and their
wider adoption in industry. Despite significant interest this shortcoming has
not been adequately addressed. This study proposes a novel framework called
Hide-and-Seek (HnS) for training Interpretable Neural Networks and establishes
a theoretical foundation for exploring and comparing similar ideas. Extensive
experimentation indicates that a high degree of interpretability can be imputed
into Neural Networks, without sacrificing their predictive power.Comment: 24 pages, 14 figures. Submitted on a special issue for Explainable
AI, on Elsevier's "Artificial Intelligence
Fast Bayesian Uncertainty Estimation and Reduction of Batch Normalized Single Image Super-Resolution Network
Convolutional neural network (CNN) has achieved unprecedented success in
image super-resolution tasks in recent years. However, the network's
performance depends on the distribution of the training sets and degrades on
out-of-distribution samples. This paper adopts a Bayesian approach for
estimating uncertainty associated with output and applies it in a deep image
super-resolution model to address the concern mentioned above. We use the
uncertainty estimation technique using the batch-normalization layer, where
stochasticity of the batch mean and variance generate Monte-Carlo (MC) samples.
The MC samples, which are nothing but different super-resolved images using
different stochastic parameters, reconstruct the image, and provide a
confidence or uncertainty map of the reconstruction. We propose a faster
approach for MC sample generation, and it allows the variable image size during
testing. Therefore, it will be useful for image reconstruction domain. Our
experimental findings show that this uncertainty map strongly relates to the
quality of reconstruction generated by the deep CNN model and explains its
limitation. Furthermore, this paper proposes an approach to reduce the model's
uncertainty for an input image, and it helps to defend the adversarial attacks
on the image super-resolution model. The proposed uncertainty reduction
technique also improves the performance of the model for out-of-distribution
test images. To the best of our knowledge, we are the first to propose an
adversarial defense mechanism in any image reconstruction domain.Comment: To appear in the Proceedings of the IEEE Conference on Computer
Vision and Pattern Recognition (CVPR 2021
Understanding the Mechanisms of Deep Transfer Learning for Medical Images
The ability to automatically learn task specific feature representations has
led to a huge success of deep learning methods. When large training data is
scarce, such as in medical imaging problems, transfer learning has been very
effective. In this paper, we systematically investigate the process of
transferring a Convolutional Neural Network, trained on ImageNet images to
perform image classification, to kidney detection problem in ultrasound images.
We study how the detection performance depends on the extent of transfer. We
show that a transferred and tuned CNN can outperform a state-of-the-art feature
engineered pipeline and a hybridization of these two techniques achieves 20\%
higher performance. We also investigate how the evolution of intermediate
response images from our network. Finally, we compare these responses to
state-of-the-art image processing filters in order to gain greater insight into
how transfer learning is able to effectively manage widely varying imaging
regimes.Comment: Published in MICCAI Workshop on Deep Learning in Medical Image
Analysis, 201
Position paper: a general framework for applying machine learning techniques in operating room
In this position paper we describe a general framework for applying machine
learning and pattern recognition techniques in healthcare. In particular, we
are interested in providing an automated tool for monitoring and incrementing
the level of awareness in the operating room and for identifying human errors
which occur during the laparoscopy surgical operation. The framework that we
present is divided in three different layers: each layer implements algorithms
which have an increasing level of complexity and which perform functionality
with an higher degree of abstraction. In the first layer, raw data collected
from sensors in the operating room during surgical operation, they are
pre-processed and aggregated. The results of this initial phase are transferred
to a second layer, which implements pattern recognition techniques and extract
relevant features from the data. Finally, in the last layer, expert systems are
employed to take high level decisions, which represent the final output of the
system
Long-Bone Fracture Detection using Artificial Neural Networks based on Line Features of X-ray Images
Two line-based fracture detection scheme are developed and discussed, namely
Standard line-based fracture detection and Adaptive Differential Parameter
Optimized (ADPO) line-based fracture detection. The purpose for the two
line-based fracture detection schemes is to detect fractured lines from X-ray
images using extracted features based on recognised patterns to differentiate
fractured lines from non-fractured lines. The difference between the two
schemes is the detection of detailed lines. The ADPO scheme optimizes the
parameters of the Probabilistic Hough Transform, such that granule lines within
the fractured regions are detected, whereas the Standard scheme is unable to
detect them. The lines are detected using the Probabilistic Hough Function, in
which the detected lines are a representation of the image edge objects. The
lines are given in the form of points, (x,y), which includes the starting and
ending point. Based on the given line points, 13 features are extracted from
each line, as a summary of line information. These features are used for
fracture and non-fracture classification of the detected lines. The
classification is carried out by the Artificial Neural Network (ANN). There are
two evaluations that are employed to evaluate both the entirety of the system
and the ANN. The Standard Scheme is capable of achieving an average accuracy of
74.25%, whilst the ADPO scheme achieved an average accuracy of 74.4%. The ADPO
scheme is opted for over the Standard scheme, however it can be further
improved with detected contours and its extracted features
Regularized Fuzzy Neural Networks to Aid Effort Forecasting in the Construction and Software Development
Predicting the time to build software is a very complex task for software
engineering managers. There are complex factors that can directly interfere
with the productivity of the development team. Factors directly related to the
complexity of the system to be developed drastically change the time necessary
for the completion of the works with the software factories. This work proposes
the use of a hybrid system based on artificial neural networks and fuzzy
systems to assist in the construction of an expert system based on rules to
support in the prediction of hours destined to the development of software
according to the complexity of the elements present in the same. The set of
fuzzy rules obtained by the system helps the management and control of software
development by providing a base of interpretable estimates based on fuzzy
rules. The model was submitted to tests on a real database, and its results
were promissory in the construction of an aid mechanism in the predictability
of the software construction
An Introduction to Deep Visual Explanation
The practical impact of deep learning on complex supervised learning problems
has been significant, so much so that almost every Artificial Intelligence
problem, or at least a portion thereof, has been somehow recast as a deep
learning problem. The applications appeal is significant, but this appeal is
increasingly challenged by what some call the challenge of explainability, or
more generally the more traditional challenge of debuggability: if the outcomes
of a deep learning process produce unexpected results (e.g., less than expected
performance of a classifier), then there is little available in the way of
theories or tools to help investigate the potential causes of such unexpected
behavior, especially when this behavior could impact people's lives. We
describe a preliminary framework to help address this issue, which we call
"deep visual explanation" (DVE). "Deep," because it is the development and
performance of deep neural network models that we want to understand. "Visual,"
because we believe that the most rapid insight into a complex multi-dimensional
model is provided by appropriate visualization techniques, and "Explanation,"
because in the spectrum from instrumentation by inserting print statements to
the abductive inference of explanatory hypotheses, we believe that the key to
understanding deep learning relies on the identification and exposure of
hypotheses about the performance behavior of a learned deep model. In the
exposition of our preliminary framework, we use relatively straightforward
image classification examples and a variety of choices on initial configuration
of a deep model building scenario. By careful but not complicated
instrumentation, we expose classification outcomes of deep models using
visualization, and also show initial results for one potential application of
interpretability.Comment: Accepted at NIPS 2017 - Workshop Interpreting, Explaining and
Visualizing Deep Learnin
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