The innovation power of deep learning and computer vision now reaches marine science. This seminar introduces you to the recent achievement of deep learning in marine science,especially in analyzing the echo sounder data, known as SONAR data. Changkyu Choi is a PhD student in UiT Machine Learning Group and SFI Visual Intelligence, working for novel deep learning methods that bridge computer visionto marine science. His work is also closely collaborated with the stake holders of SFI Visual Intelligence, e.g., Institute of Marine Research(Havforsknings instituttet) and Norwegian Computing Center (Norsk Regnesentral).
Urban maps in Norway are currently updated using manual photo interpretation on stereo aerial imagery. However, there is often a substantial delay after completion of construction work until new buildings, roads, etc. appear in updated versions of the urban maps. Automated pixel-based urban land cover classification from multispectral aerial images of very high resolution has proven difficult since the same spectral values may occur within several land cover types. Airborne hyperspectral data may provide better discriminative power. However, there is still the problem that the same types of material may exist within different land cover types, such as buildings, roads, parks, gardens, etc. In this seminar Øyvind Trier dives into how these challenges can be assessed using deep learning.
Director Robert Jenssen gave a talk and took part in a panel debate at the Norwegian Hospital Pharmacists Association’s conference on how AI and digitalization will impact future drug treatment.
Machine learning methods, such as deep neural networks, have been shown to be very successful for prediction in many different applications. Standard use of such methods do however not account for or underestimate the full uncertainty related to these predictions. The Bayesian approach allows for a formal way of making proper uncertainty quantification. Recently, such methods have also gained popularity within the machine learning community. In this talk Professor Geir Olve Storvik from UiO will describe how the Bayesian methodology can be applied to machine learning.We will discuss both advantages and challenges related to apply such methods in practice.
The research area of probability calibration refers to a set of work that focuses on the uncertainty and confidence of model predictions. On the top level, we want the models to be well-calibrated on the predicted probabilities. That is, the target variable should follow closely to the distribution as indicated by every distinct prediction. In this talk, Research Associate Hao Song from University of Bristol will provide an overview of the research area, including typical definitions, evaluation measures, and approaches that can improve the level of calibration.
Bayesian Neural Networks are an alternative approach to classic NN models, offering "built-in" uncertainty measures and convenient regularization. Performing inference on a BNN results in a joint posterior distribution of network parameters, which can provide insight into what makes for a well-specified network for a given problem. Master student at UiT, Jonathan Edward Berezowski, discusses how to define a BNN with these features and introduce the method of Reversible Jump Markov Chain Monte Carlo as one potential approach to inference.
Many deep learning studies are not designed to provide unbiased estimation of the system's performance in the intended application. Reports of overoptimistic estimates and opportunities may inflate the expectation of what is currently possible, misguide resource allocation, and hamper the progression of the field. In this talk, we will look into how the performance of a deep learning system in an intended application could be estimated more reliably than what is currently common practice, even if restricted to using retrospective data.
The recent advances in deep learning and drastic increase in number of imaging satellites, new levels of automation are possible and necessary. KSAT is investing significantly into modern MLOps practices to achieve this and intends to use its membership in Visual Intelligence to solve the research aspects of this transformation.
Deep learning can bring time savings and increased reproducibility to medical image analysis. However, acquiring training data is challenging due to the time-intensive nature of labeling and high inter-observer variability in annotations. Rather than labeling images, in this work we propose an alternative pipeline where images are generated from existing high-quality annotations using generative adversarial networks (GANs). Annotations are derived automatically from previously built anatomical models and transformed into realistic synthetic ultrasound images with CycleGAN.
In fisheries acoustics, echo sounding is applied to detect fish and other marine objects in the ocean; a central tool for stock assessments and establishing fishing quotas. Fish detection and species classification from echo sounder data is typically a manual process. In our work, we automate this process by training a convolutional neural network for semantic segmentation using supervised learning. The talk will describe the data, the CNN-approach used for segmentation – as well as issues related to the training data, such as the quality of annotations when used in a machine learning setting.