FarmAI – Artificial intelligence for the farming simulator

For the globally successful "Farming simulator" video game series developed by GIANTS Software GmbH, a new, continually entertaining, easily extendable game mode has been made possible through artificial intelligence (AI). In this project, reinforcement learning algorithms are used to find suitable action strategies by simulating games.

Combining Reinforcement Learning with Supervised Deep Learning for Neural Active Scene Understanding

While in living beings, vision is an active process whereby image acquisition and classification are intertwined to gradually refine perception, much of today’s computer vision is built on the inferior paradigm of episodic classification of i.i.d. samples. Our aim is to enable improved scene understanding for robots while taking the sequential nature of seeing over time into account. We present a combined supervised and reinforcement learning multi-task approach to answer questions about different aspects of a scene, such as the relationship between objects, their quantity or their relative positions to the camera.

RealScore - Scanning of Real-World Sheet Music for a Digital Music Stand

We have built a sheet music scanning service that ensures high quality input. To increase market penetration, we plan to extend its use to smartphone images, used sheets and other sources. Project RealScore enhances the successful precursor project by making deep learning adapt to previously unseen data through unsupervised learning.

Deep Learning in the Wild

Due to the methodologies vast success on a wide range of machine perception tasks, deep learning with neural networks is applied by an increasing number of actors outside classic research environments. While this interest is fuelled by exciting success stories, practical work in deep learning on novel tasks without existing baselines remains challenging. This paper explores specific challenges that arise in the realm of real-world tasks, based on case studies from research and development in conjunction with industry, and extracts the lessons learned.

QualitAI - Quality control of industrial products via deep learning on images

QualitAI researches and develops a device for the automatic quality control of industrial products such as cardiac balloon catheters. This is facilitated through innovation in analysing camera images using deep learning, specifically in rendering the resulting model robust and interpretable.

Trace and Detect Adversarial Attacks on CNNs using Feature Response Maps

The existence of adversarial attacks on convolutional neural networks (CNN) raises doubts about the fitness of such models for serious applications. Such attacks can manipulate input images so that misclassification is evoked while the images continue to look normal to the human observer—they are therefore not easily detectable. In a different context, backpropagated activations of CNN hidden layers—“feature responses” to a given input—have been helpful in visualising for a human “debugger” what the CNN “looks at” while computing its output. In this paper, we propose a novel detection method for adversarial examples to prevent attacks.

Pilot study machine learning for injection molding processes

Researchers from the CAI and InES are jointly investigating the opportunities for bundling process knowledge about injection molding processes in neural networks and transferring it to new application scenarios as part of a technical deep dive. The groups of Prof. Stadelmann (Computer Vision, Perception & Cognition, ZHAW CAI) and Prof. Rosenthal (Realtime Platforms, ZHAW InES)- recently teamed up with the Kistler Innovation Lab to explore risks and opportunities for improving automatic quality control and process monitoring in plastic injection molding using advanced machine learning. In particular, transfer learning / domain adaptation and continuous learning in neural networks are being evaluated for small data scenarios. The team secured funding for a technical deep dive through the NTN Databooster from the Data Innovation Alliance and Innosuisse for a three-month study. The results will inform the design and implementation of future Kistler products in another joint project. The NTN Databooster supports data-driven innovation by helping companies find suitable research partners for their challenges, identify new solutions, assess feasibility and risks in technical depths and finally apply for appropriate funding for joint R&D projects. CAI staff members have been active in various capacities since the founding of Databooster and its supporting organisation, the Data Innovation Alliance, and have successfully completed several R&D projects in this framework.

Design patterns for resource-constrained automated deep-learning methods

We present an extensive evaluation of a wide variety of promising design patterns for automated deep-learning (AutoDL) methods, organized according to the problem categories of the 2019 AutoDL challenges, which set the task of optimizing both model accuracy and search efficiency under tight time and computing constraints. We propose structured empirical evaluations as the most promising avenue to obtain design principles for deep-learning systems due to the absence of strong theoretical support. From these evaluations, we distill relevant patterns which give rise to neural network design recommendations. In particular, we establish (a) that very wide fully connected layers learn meaningful features faster; we illustrate (b) how the lack of pretraining in audio processing can be compensated by architecture search; we show (c) that in text processing deep-learning-based methods only pull ahead of traditional methods for short text lengths with less than a thousand characters under tight resource limitations; and lastly we present (d) evidence that in very data- and computing-constrained settings, hyperparameter tuning of more traditional machine-learning methods outperforms deep-learning systems.

Virtual Kids

By developing child-like avatars for the training of interrogators of children, this project manages to close significant knowledge gaps regarding the effectiveness of individual training elements and personal influencing variables. The findings and the training tool can be used for education and training purposes as well as for the recruitment of personnel. The project’s findings can be used as a basis to improve interrogation practices and help to meet the international demand for a child-friendly justice system.

Survey on evaluation methods for dialogue systems

In this paper, we survey methods and concepts developed for the evaluation of dialogue systems. Evaluation, in and of itself, is a crucial part of the development process. Often, dialogue systems are assessed by means of human evaluation and questionnaires. However, these methods tend to be very expensive and time-consuming, which is why there have been intense efforts to find methods that reduce the involvement of human labour.