Autonomous Driving and Advance Driver Assistance Systems (ADAS) are revolutionizing the way we drive and the future of mobility. Among ADAS, Traffic Sign Classification is an important technique which assists the driver to easily interpret traffic signs on the road. In this thesis, we used the powerful combination of Image Processing and Deep Learning to pre-process and classify the traffic signs. Recent studies in Deep Learning show us how good a Convolutional Neural Network (CNN) is for image classification and there are several state-of-the-art models with classification accuracies over 99 % existing out there. This shaped our thesis to focus more on tackling the current challenges and some open-research cases. We focussed more on performance tuning by modifying the existing architectures with a trade-off between computations and accuracies. Our research areas include enhancement in low light/noisy conditions by adding Recurrent Neural Network (RNN) connections, and contribution to a universal-regional dataset with Generative Adversarial Networks (GANs). The results obtained on the test data are comparable to the state-of-the-art models and we reached accuracies above 98% after performance evaluation in different frameworks

abstract: Image-based process monitoring has recently attracted increasing attention due to the advancement of the sensing technologies. However, existing process monitoring methods fail to fully utilize the spatial information of images due to their complex characteristics including the high dimensionality and complex spatial structures. Recent advancement of the unsupervised deep models such as a generative adversarial network (GAN) and generative adversarial autoencoder (AAE) has enabled to learn the complex spatial structures automatically. Inspired by this advancement, we propose an anomaly detection framework based on the AAE for unsupervised anomaly detection for images. AAE combines the power of GAN with the variational autoencoder, which serves as a nonlinear dimension reduction technique with regularization from the discriminator. Based on this, we propose a monitoring statistic efficiently capturing the change of the image data. The performance of the proposed AAE-based anomaly detection algorithm is validated through a simulation study and real case study for rolling defect detection. / Dissertation/Thesis / Masters Thesis Industrial Engineering 2019

Hlboké neurónové siete sa bežne používajú v oblasti odstraňovania šumu. Trénovací proces neurónovej siete je možné rožšíriť využitím druhej neurónovej siete, ktorej cieľom je vložiť šum do čistej rečovej nahrávky. Tieto dve siete sa môžu spolu využiť k rekonštrukcii pôvodných čistých a zašumených nahrávok. Táto práca skúma efektivitu tejto techniky, zvanej cyklická konzistencia. Cyklická konzistencia zlepšuje robustnosť neurónovej siete bez toho, aby sa daná sieť akokoľvek modifikovala, nakoľko vystavuje sieť na odstraňovanie šumu rôznorodejšiemu množstvu zašumených dát. Avšak, táto technika vyžaduje trénovacie dáta skladajúce sa z párov vstupných a referenčných nahrávok. Tieto dáta niesu vždy dostupné. Na trénovanie modelov s nepárovanými dátami využívame generatívne neurónové siete s cyklickou konzistenciou. V tejto práci sme vykonali veľké množstvo experimentov s modelmi trénovanými na párovaných a nepárovaných dátach. Naše výsledky ukazujú, že využitie cyklickej konzistencie výrazne zlepšuje výkonnosť modelov.

Systémy založené na biometrickej autentizácii sa stávajú súčasťou nášho každodenného bytia. Tieto systémy však nedovoľujú používateľom priamo alebo nepriamo meniť spôsob, akým sa k ich dátam pristupuje a ako sa s nimi bude zaobchádzať ďalej v budúcnosti. Dôsledkom tohto môžu vyplynúť riziká spojené s uniknutím identity jedinca. Táto práca sa zaoberá návrhom systému, ktorý zachováva privátnosť a zároveň umožňuje autentizáciu na základe biometrických čŕt používateľov, a to za pomoci generatívnej neurónovej siete (GAN). V práci sa konkrétne uvažuje o tom, že GAN je použitá na transformáciu obrázkov tvárí napríklad na obrázky kvetov. Autentizačný systém sídliaci na serveri je v konečnom dôsledku učený rozlišovať používateľov podľa obrázkov kvetov a nie tvárí. Na základe vykonaných experimentov môžeme potvrdiť, že navrhovaná metóda je robustná voči útokom, pričom stále vykazuje kvalitatívne požiadavky kladené na štandardný autentizačný systém.

The e-commerce industry frequently has to deal with displaying product images in a website where the images are provided by the selling partners. The images in question can have drastically different aspect ratios and resolutions which makes it harder to present them while maintaining a coherent user experience. Manipulating images by cropping can sometimes result in parts of the foreground (i.e. product or person within the image) to be cut off. Image outpainting is a technique that allows images to be extended past its boundaries and can be used to alter the aspect ratio of images. Together with object detection for locating the foreground makes it possible to manipulate images without sacrificing parts of the foreground. For image outpainting a deep learning model was trained on product images that can extend images by at least 25%. The model achieves 8.29 FID score, 44.29 PSNR score and 39.95 BRISQUE score. For testing this solution in practice a simple image manipulation pipeline was created which uses image outpainting when needed and it shows promising results. Images can be manipulated in under a second running on ZOTAC GeForce RTX 3060 (12GB) GPU and a few seconds running on a Intel Core i7-8700K (16GB) CPU. There is also a special case of images where the background has been digitally replaced with a solid color and they can be outpainted even faster without deep learning.

Generative Adversarial Networks (GAN) is a technique used to learn the distribution of some dataset in order to generate similar data. GAN models are notoriously difficult to train, which has caused limited deployment in the industry. The results of this study can be used to accelerate the process of making GANs production ready. An experiment was conducted where multiple GAN models were trained, with the hyperparameters Leaky ReLU alpha, convolutional filters, learning rate and batch size as independent variables. A Mann-Whitney U-test was used to compare the training time and training stability of each model to the others’. Except for the Leaky ReLU alpha, changes to the investigated hyperparameters had a significant effect on the training time and stability. This study is limited to a few hyperparameters and values, a single dataset and few data points, further research in the area could look at the generalisability of the results or investigate more hyperparameters.

Generative Artificial Intelligence is a field of artificial intelligence where systems can learn underlying patterns in previously seen content and generate new content. This thesis explores a generative artificial intelligence technique used for image-toimage translations called Cycle-consistent Adversarial network (CycleGAN), which can translate images from one domain into another. The CycleGAN is a stateof-the-art technique for doing unsupervised image-to-image translations. It uses the concept of cycle-consistency to learn a mapping between image distributions, where the Mean Absolute Error function is used to compare images and thereby learn an underlying mapping between the two image distributions. In this work, we propose to use the Structural Similarity Index Measure (SSIM) as an alternative to the Mean Absolute Error function. The SSIM is a metric inspired by human perception, which measures the difference in two images by comparing the difference in, contrast, luminance, and structure. We examine if using the SSIM as the cycle-consistency loss in the CycleGAN will improve the image quality of generated images as measured by the Inception Score and Fréchet Inception Distance. The inception Score and Fréchet Inception Distance are both metrics that have been proposed as methods for evaluating the quality of images generated by generative adversarial networks (GAN). We conduct a controlled experiment to collect the quantitative metrics. Our results suggest that using the SSIM in the CycleGAN as the cycle-consistency loss will, in most cases, improve the image quality of generated images as measured Inception Score and Fréchet Inception Distance.

I en framtid där förutsägelser och beslut som tas av maskininlärningssystem överträffar människors förmåga behöver systemen att vara tolkbara för att vi skall kunna lita på och förstå dem. Vår studie utforskar världen av tolkbar maskininlärning genom att designa och undersöka artefakter. Vi genomför experiment för att utforska förklarbarhet, tolkbarhet samt tekniska utmaningar att skapa maskininlärningsmodeller för att identifiera liknande men unika objekt. Slutligen genomför vi ett användartest för att utvärdera toppmoderna förklaringsverktyg i ett direkt mänskligt sammanhang. Med insikter från dessa experiment diskuterar vi den potentiella framtiden för detta fält / In a future where predictions and decisions made by machine learning systems outperform humans we need the systems to be interpretable in order for us to trust and understand them. Our study explore the realm of interpretable machine learning through designing artifacts. We conduct experiments to explore explainability, interpretability as well as technical challenges of creating machine learning models to identify objects that appear similar to humans. Lastly, we conduct a user test to evaluate current state-of-the-art visual explanatory tools in a human setting. From these insights, we discuss the potential future of this field.

Usability testing within User Interface (UI) is a central part of assuring high-quality UIdesign that provides good user-experiences across multiple user-groups. The processof usability testing often times requires extensive collection of user feedback, preferablyacross multiple user groups, to ensure an unbiased observation of the potential designflaws within the UI design. Attaining feedback from certain user groups has shown tobe challenging, due to factors such as medical conditions that limits the possibilities ofusers to participate in the usability test. An absence of these hard-to-access groups canlead to designs that fails to consider their unique needs and preferences, which maypotentially result in a worse user experience for these individuals. In this thesis, wetry to address the current gaps within data collection of usability tests by investigatingwhether the Generative Adversarial Network (GAN) framework can be used to generatehigh-quality synthetic user interactions of a particular UI gesture across multiple usergroups. Moreover, a collection UI interaction of 2 user groups, namely the elderlyand young population, was conducted where the UI interaction at focus was thedrag-and-drop operation. The datasets, comprising of both user groups were trainedon separate GANs, both using the doppelGANger architecture, and the generatedsynthetic data were evaluated based on its diversity, how well temporal correlations arepreserved and its performance compared to the real data when used in a classificationtask. The experiment result shows that both GANs produces high-quality syntheticresemblances of the drag-and-drop operation, where the synthetic samples show bothdiversity and uniqueness when compared to the actual dataset. The synthetic datasetacross both user groups also provides similar statistical properties within the originaldataset, such as the per-sample length distribution and the temporal correlationswithin the sequences. Furthermore, the synthetic dataset shows, on average, similarperformance achievements across precision, recall and F1 scores compared to theactual dataset when used to train a classifier to distinguish between the elderly andyounger population drag-and-drop sequences. Further research regarding the use ofmultiple UI gestures, using a single GAN to generate UI interactions across multipleuser groups, and performing a comparative study of different GAN architectures wouldprovide valuable insights of unexplored potentials and possible limitations within thisparticular problem domain.

In digital pathology, hematoxylin & eosin (H&E) is a routine stain which is performed on most clinical cases and it often provides clinicians with sufficient information for diagnosis. However, when making decisions on how to guide breast cancer treatment, immunohistochemical staining of human epidermal growth factor 2 (HER2 staining) is also needed. Over-expression of the HER2 protein plays a significant role in the progression of breast cancer and is therefore important to consider during treatment planning. However, the downside of HER2 staining is that it is both time consuming and rather expensive. This thesis explores the possibility for H&E-to-HER2 stain translation using generative adversarial networks (GANs). If effective, this has the potential to reduce the costs and time spent on tissue processing while still providing clinicians with the images necessary to make a complete diagnosis. To explore this area two supervised (Pix2Pix, PyramidPix2Pix) and one unsupervised (cycleGAN) GAN structure was implemented and trained on digital pathology images from the MIST dataset. These models were trained two times, with 256x256 and 512x512 patches, to see which effect patch size has on stain translation performance as well. In addition, a methodology for evaluating the quality of the generated HER2 patches was also presented and utilized. This methodology consists of structural similarity index (SSIM) and peak signal to noise ratio (PSNR) comparison to the ground truth, and a HER2 status classification protocol. In the latter a classification tool provided by Sectra was used to assign each patch with a HER2 status of No tumor, 1+, 2+ or 3+ and the statuses of the generated patches were then compared to the statuses of the ground truths. The results show that the supervised Pyramid Pix2Pix model trained on 512x512 patches performs the best according to the SSIM and PSNR metrics. However, the unsupervised cycleGAN model shows more promising results when it comes to both visual assessment and the HER2 status classification protocol. Especially when trained on 256x256 patches for 200 epochs which gave an accuracy of 0.655, F1-score of 0.674 and MCC of 0.490. In conclusion the HER2 status classification protocol is deemed as a suitable way to evaluate H&E-to-HER2 stain translation and thereby the unsupervised method is considered to be better than the supervised. Moreover, it is also concluded that a smaller patch size result in worse translation of cellular structure for the supervised methods. Further studies should focus on incorporating HER2 status classification in the cycleGAN loss function and more extensive training runs to further improve the quality of H&E-to-HER2 stain translation.