Electronic Theses and Dissertations (Masters)

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Evaluating Pre-training Mechanisms in Deep Learning Enabled Tuberculosis Diagnosis
(University of the Witwatersrand, Johannesburg, 2024) Zaranyika, Zororo; Klein, Richard
Tuberculosis (TB) is an infectious disease caused by a bacteria called Mycobacterium Tuberculosis. In 2021, 10.6 million people fell ill because of TB and about 1.5 million lives are lost from TB each year even though TB is a preventable and curable disease. The latest global trends in TB death cases are shown in 1.1. To ensure a higher survival rate and prevent further transmissions, it is important to carry out early diagnosis. One of the critical methods of TB diagnosis and detection is the use of posterior-anterior chest radiographs (CXR). The diagnosis of Tuberculosis and other chest-affecting dis- eases like Pneumoconiosis is time-consuming, challenging and requires experts to read and interpret chest X-ray images, especially in under-resourced areas. Various attempts have been made to perform the diagnosis using deep learning methods such as Convolutional Neural Networks (CNN) using labelled CXR images. Due to the nature of CXR images in maintaining a consistent structure and overlapping visual appearances across different chest-affecting diseases, it is reasonable to believe that visual features learned in one disease or geographic location may transfer to a new TB classificationmodel. This would allow us to leverage large volumes of labelled CXR images available online hence decreasing the data required to build a local model. This work will explore to what extent such pre-training and transfer learning is useful and whether it may help decrease the data required for a locally trained classifier. In this research, we investigated various pre-training regimes using selected online datasets to under- stand whether the performance of such models can be generalised towards building a TB computer-aided diagnosis system and also inform us on the nature and size of CXR datasets we should be collecting. Our experiment results indicated that both supervised and self-supervised pre-training between the CXR datasets cannot significantly improve the overall performance metrics of a TB. We noted that pre-training on the ChestX-ray14, CheXpert, and MIMIC-CXR datasets resulted in recall values of over 70% and specificity scores of at least 90%. There was a general decline in performance in our experiments when we pre-trained on one dataset and fine-tuned on a different dataset, hence our results were lower than baseline experiment results. We noted that ImageNet weights initialisation yields superior results over random weights initialisation on all ex- periment configurations. In the case of self-supervised pre-training, the model reached acceptable metrics with a minimum number of labels as low as 5% when we fine-tuned on the TBX11k dataset, although slightly lower in performance compared to the super-vised pre-trained models and the baseline results. The best-performing self-supervised pre-trained model with the least number of training labels was the MoCo-ResNet-50 model pre-trained on the VinDr-CXR and PadChest datasets. These model configura- tions achieved recall scores of 81.90% and a specificity score of 81.99% on VinDr-CXR pre-trained weights while the PadChest weights scored a recall of 70.29% and a speci- ficity of 70.22%. The other self-supervised pre-trained models failed to reach scores of at least 50% on both recall or specificity with the same number of labels
Generating Rich Image Descriptions from Localized Attention
(University of the Witwatersrand, Johannesburg, 2023-08) Poulton, David; Klein, Richard
The field of image captioning is constantly growing with swathes of new methodologies, performance leaps, datasets, and challenges. One new challenge is the task of long-text image description. While the vast majority of research has focused on short captions for images with only short phrases or sentences, new research and the recently released Localized Narratives dataset have pushed this to rich, paragraph length descriptions. In this work we perform additional research to grow the sub-field of long-text image descriptions and determine the viability of our new methods. We experiment with a variety of progressively more complex LSTM and Transformer-based approaches, utilising human-generated localised attention traces and image data to generate suitable captions, and evaluate these methods on a suite of common language evaluation metrics. We find that LSTM-based approaches are not well suited to the task, and under-perform Transformer-based implementations on our metric suite while also proving substantially more demanding to train. On the other hand, we find that our Transformer-based methods are well capable of generating captions with rich focus over all regions of the image and in a grammatically sound manner, with our most complex model outperforming existing approaches on our metric suite.
Generative Model Based Adversarial Defenses for Deepfake Detectors
(University of the Witwatersrand, Johannesburg, 2023-08) Kavilan Dhavan, Nair; Klein, Richard
Deepfake videos present a serious threat to society as they can be used to spread mis-information through social media. Convolutional Neural Networks (CNNs) have been effective in detecting deepfake videos, but they are vulnerable to adversarial attacks that can compromise their accuracy. This vulnerability can be exploited by deepfake creators to evade detection. In this study, we evaluate the effectiveness of two genera- tive adversarial defense mechanisms, APE-GAN and MagNet, in the context of deepfake detection. We use the FaceForensics++ dataset and a CNN victim model based on the XceptionNet architecture, which we attack using the iterative fast gradient sign method at two different levels of ✏, ✏ = 0.0001 and ✏ = 0.01. We find that both APE-GAN and MagNet can purify the adversarial images and restore the performance of the vic- tim model to within 10% of the model’s accuracy on benign fake inputs. However, these methods were less effective at restoring accuracy for adversarial real examples and were not able to significantly restore accuracy when the adversarial attack was aggressive (✏ = 0.01). We recommend that an adversarial defense method be used in conjunction with a deepfake detector to improve the accuracy of predictions. APE-GAN and MagNet are effective methods in the deepfake context, but their effectiveness is limited when the adversarial attack is aggressive.
Improving audio-driven visual dubbing solutions using self-supervised generative adversarial networks
(University of the Witwatersrand, Johannesburg, 2023-09) Ranchod, Mayur; Klein, Richard
Audio-driven visual dubbing (ADVD) is the process of accepting a talking-face video, along with a dubbing audio segment, as inputs and producing a dubbed video such that the speaker appears to be uttering the dubbing audio. ADVD aims to address the language barrier inherent in the consumption of video-based content caused by the various languages in which videos may be presented. Specifically, a video may only be consumed by the audience that is familiar with the spoken language. Traditional solutions, such as subtitles and audio-dubbing, hinder the viewer’s experience by either obstructing the on-screen content or introducing an unpleasant discrepancy between the speaker’s mouth movements and the input dubbing audio, respectively. In contrast, ADVD strives to achieve a natural viewing experience by synchronizing the speaker’s mouth movements with the dubbing audio. A comprehensive survey of several ADVD solutions revealed that most existing solutions achieve satisfactory visual quality and lip-sync accuracy but are limited to low-resolution videos with frontal or near frontal faces. Since this is in sharp contrast to real-world videos, which are high-resolution and contain arbitrary head poses, we present one of the first ADVD solutions trained with high-resolution data and also introduce the first pose-invariant ADVD solution. Our results show that the presented solution achieves superior visual quality while also achieving high measures of lip-sync accuracy, consequently enabling the solution to achieve significantly improved results when applied to real-world videos.
Learning to adapt: domain adaptation with cycle-consistent generative adversarial networks
(University of the Witwatersrand, Johannesburg, 2023) Burke, Pierce William; Klein, Richard
Domain adaptation is a critical part of modern-day machine learning as many practitioners do not have the means to collect and label all the data they require reliably. Instead, they often turn to large online datasets to meet their data needs. However, this can often lead to a mismatch between the online dataset and the data they will encounter in their own problem. This is known as domain shift and plagues many different avenues of machine learning. From differences in data sources, changes in the underlying processes generating the data, or new unseen environments the models have yet to encounter. All these issues can lead to performance degradation. From the success in using Cycle-consistent Generative Adversarial Networks(CycleGAN) to learn unpaired image-to-image mappings, we propose a new method to help alleviate the issues caused by domain shifts in images. The proposed model incorporates an adversarial loss to encourage realistic-looking images in the target domain, a cycle-consistency loss to learn an unpaired image-to-image mapping, and a semantic loss from a task network to improve the generator’s performance. The task network is con-currently trained with the generators on the generated images to improve downstream task performance on adapted images. By utilizing the power of CycleGAN, we can learn to classify images in the target domain without any target domain labels. In this research, we show that our model is successful on various unsupervised domain adaptation (UDA) datasets and can alleviate domain shifts for different adaptation tasks, like classification or semantic segmentation. In our experiments on standard classification, we were able to bring the models performance to near oracle level accuracy on a variety of different classification datasets. The semantic segmentation experiments showed that our model could improve the performance on the target domain, but there is still room for further improvements. We also further analyze where our model performs well and where improvements can be made.
MultiI-View Ranking: Tasking Transformers to Generate and Validate Solutions to Math Word Problems
(University of the Witwatersrand, Johannesburg, 2023-11) Mzimba, Rifumo; Klein, Richard; Rosman, Benjamin
The recent developments and success of the Transformer model have resulted in the creation of massive language models that have led to significant improvements in the comprehension of natural language. When fine-tuned for downstream natural language processing tasks with limited data, they achieve state-of-the-art performance. However, these robust models lack the ability to reason mathematically. It has been demonstrated that, when fine-tuned on the small-scale Math Word Problems (MWPs) benchmark datasets, these models are not able to generalize. Therefore, to overcome this limitation, this study proposes to augment the generative objective used in the MWP task with complementary objectives that can assist the model in reasoning more deeply about the MWP task. Specifically, we propose a multi-view generation objective that allows the model to understand the generative task as an abstract syntax tree traversal beyond the sequential generation task. In addition, we propose a complementary verification objective to enable the model to develop heuristics that can distinguish between correct and incorrect solutions. These two goals comprise our multi-view ranking (MVR) framework, in which the model is tasked to generate the prefix, infix, and postfix traversals for a given MWP, and then use the verification task to rank the generated expressions. Our experiments show that the verification objective is more effective at choosing the best expression than the widely used beam search. We further show that when our two objectives are used in conjunction, they can effectively guide our model to learn robust heuristics for the MWP task. In particular, we achieve an absolute percentage improvement of 9.7% and 5.3% over our baseline and the state-of-the-art models on the SVAMP datasets. Our source code can be found on https://github.com/ProxJ/msc-final.
Pipeline for the 3D Reconstruction of Rigid, Handheld Objects through the Use of Static Cameras
(University of the Witwatersrand, Johannesburg, 2023-04) Kambadkone, Saatwik Ramakrishna; Klein, Richard
In this paper, we develop a pipeline for the 3D reconstruction of handheld objects using a single, static RGB-D camera. We also create a general pipeline to describe the process of handheld object reconstruction. This general pipeline suggests the deconstruction of this task into three main constituents: input, where we decide our main method of data capture; segmentation and tracking, where we identify and track the relevant parts of our captured data; and reconstruction where we develop a method for reconstructing our previous information into 3D models. We successfully create a handheld object reconstruction method using a depth sensor as our input; hand tracking, depth segmentation and optical flow to retrieve relevant information; and reconstruction through the use of ICP and TSDF maps. During this process, we also evaluate other possible variations of this successful method. In one of these variations, we test the effect of using depth-estimation to generate data as- the input to our pipeline. While this experimentation helps us quantify our method’s robustness to noise in the input data, we do conclude that current depth estimation techniques do not provide adequate detail for the reconstruction of handheld objects.

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Browsing Electronic Theses and Dissertations (Masters) by Author "Klein, Richard"

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