Product-ProtoNet: A simple architecture for classifying supermarket products, using just a few example images

Airlab, a collaboration between TU Delft and Ahold Delhaize, is developing Albert, a robot tailored to work in a complex supermarket environment. Key to Albert is a product detection and classification module that tells it what products to grasp and where they are located in a shelf. Albert’s existing YOLO‑based product detector a significant...

Data-efficient resolution transfer with Continuous Kernel Convolutional Neural Networks

Convolutional Neural Networks (CNNs) benefit from fine-grained details in high-resolution images, but these images are not always easily available as data collection can be expensive or time-consuming. Transfer learning pre-trains models on data from a related domain before fine-tuning on the main domain, and is a common strategy to deal with...

Augment it Maybe?: Improving Deep Vision Models with Adversarial Scene Text Augmentation

Image data augmentation has been regarded as a reliable and effective way to increase the data available for training. With the advent and rise of Generative AI, generative data augmentation has been shown to realize even better gains in performance for downstream tasks. However, these performance gains are often the cause of "extra information"...

Natural Language Processing and Tabular Data sets in Federated Continual Learning: A usability study of FCL in domains beyond Image classification

Federated Continual Learning (FCL) is a emerging field with strong roots in Image classification. However, limited research has been done on its potential in Natural Language Processing and Tabular datasets. With recent developments in A.I. with language models and the widespread use of mobile devices, it becomes relevant to consider FCL’s...

Extracting insights from black box neural networks for network-wide traffic predictions

Accurate and trustworthy short-term traffic prediction is crucial in the modern world for the comfort of drivers and decision-makers as it is used to improve the performance of traffic management systems, lessen congestion, increase safety, and shorten journey times. It is possible to discover useful information for network transportation...

Detection of Atmospheric Gravity Waves: Two classification approach - Image classification and meteorological feature classification

With the advent of offshore wind farms, the research into the various phenomenon that affects their performance is vast and detailed. But the effect of a particular phenomenon, atmospheric gravity waves (AGWs), on wind farm performance is limited. AGWs are oscillations of the airflow due to an imbalance in the buoyancy and gravity forces,...

Green AI: An empirical study

In this work, we look at the intersection of Sustainable Software Engineering and AI engineering known as Green AI. AI computing is rapidly becoming more expensive, calling for a change in design philosophy. We consider both training and inference of neural networks used for image vision; to reveal energy-efficient practices in an exploratory...

Using graphics to assess wellbeing in a conversational user interface

With the COVID-19 pandemic testing humanity worldwide in unforeseen ways, wellbeing assessment has stepped to the foreground of individual health status. Conversational User Interfaces (CUIs) prove promising as an assessment tool, but lacking the means to retain users engaged during the process. This research aims to explore a solution...

Flowsheet Recognition using Deep Convolutional Neural Networks

Flowsheets are the most important building blocks to define and communicate the structure of chemical processes. Gaining access to large data sets of machine-readable chemical flowsheets could significantly enhance process synthesis through artificial intelligence. A large number of these flowsheets are publicly available in the scientific...

Exploration of Deep Learning-based Computer Vision for the detection of floating plastic debris in waterways

Plastic pollution is one of the most challenging global environmental problems. Currently, more than 1000 rivers transport approximately 80% of the plastic influx into the oceans. Naturally, more and more companies are interested in tackling this problem. One of them is Noria Sustainable Innovators, a company based in Delft (Netherlands). It is...

Inferring roof semantics for more accurate solar potential assessment

Led in cooperation with the company Brink, who provides management and consultation services for construction and real estate sectors, this Synthesis Project aims at automatically deriving meaningful information about buildings. More precisely, the focus is to automatically detect roof obstacles - such as dormers, chimneys, and solar panels - to...

Backdoor Attacks in Neural Networks

Deep learning has made tremendous success in the past decade. As a result, it is becoming widely deployed in various safety and security-critical applications like autonomous driving, malware detection, fingerprint identification, and financial fraud detection. It was recently shown that deep neural networks are susceptible to multiple attacks...

Automatic algorithm selection and hyperparameter optimization for medical image classification

Recent years have shown a tremendous increase in the application of Artificial Intelligence to the field of radiology, often through the extraction and analysis of large numbers of quantitative features from medical images. These applications increase the demand for machine learning models to extract information from these images. To provide...

Classifying Retail Store Cabinets with Missing or Misplaced Products Using Verification Learning

Performing tasks in dynamic environments is still an open challenge in robotics. To be able to perform a task reliably in such scenarios, the state of the world has to be continuously monitored. In this context, most state-of-the-art perception methods focus on the recognition and classification of individual objects. However, these methods...

Global Interpretation of Image Classification Models via SEmantic Feature Analysis (SEFA)

Deep learning models have achieved state-of-the-art performance on several image classification tasks over the past years. Several studies claim to approach or even surpass human-levels of performance when using such models to classify images. However, these architectures are notoriously complex, thus making their interpretation a challenge....