Automated design of the deep neural network pipeline

Dissertation (MSc (Computer Science))--University of Pretoria, 2021.Deep neural networks have been shown to be very effective for image processing and text processing. However the big challenge is designing the deep neural network pipeline, as it is time consuming and requires machine learning expertise. More and more non-experts are using deep neural networks in their day-to-day lives, but do not have the expertise to parameter tune and construct optimal deep neural network pipelines. AutoML has mainly focused on neural architecture design and parameter tuning, but little attention has been given to optimal design of the deep neural network pipeline and all of its constituent parts. In this work a single point hyper heuristic (SPHH) was used to automate iii the design of the deep neural network pipeline. The SPHH constructed a deep neural network pipeline design by selecting techniques to use at the various stages of the pipeline, namely: the preprocessing stage, the feature engineering stage, the augmentation stage as well as selecting a deep neural network architecture and relevant hyper-parameters. This work also investigated transfer learning by using a design that was created for one dataset as a starting point for the design process for a different dataset and the effect thereof was evaluated. The reusability of the designs themselves were also tested. The SPHH designed pipelines for both the image processing and text processing domain. The image processing domain covered maize disease detection and oral lesion detection specifically and text processing used sentiment analysis and spam detection, with multiple datasets being used for all the aforementioned tasks. The pipeline designs created by means of automated design were compared to manually derived pipelines from the literature for the given datasets. This research showed that automated design of a deep neural network pipeline using a single point hyper-heuristic is effective. Deep neural network pipelines designed by the SPHH are either better than or just as good as manually derived pipeline designs in terms of performance and application time. The results showed that the pipeline designs created by the SPHH are not reusable as they do not provide comparable performance to the results achieved when specifically creating a design for a dataset. Transfer learning using the designed pipelines is found to produce results comparable to or better than the results achieved when using the SPHH without transfer learning. Transfer learning is only effective when the correct target and source are chosen, for some target datasets negative transfer occurs when using certain datasets as the transfer learning source. Future work will include applying the automated design approach to more domains and making designs reusable. The transfer learning process will also be automated in future work to ensure positive transfer occurs. The last recommendation for future work is to construct a pipeline for unsupervised deep neural network techniques instead of supervised deep neural network techniques.The work presented in this thesis is supported by the National Research Foundation of South Africa (Grant Numbers 46712). Opinions expressed and conclusions arrived at, are those of the author and are not necessarily to be attributed to the NRF.Computer ScienceMSc (Computer Science)Unrestricte

Automated design of the deep neural network pipeline

Deep neural networks have proven to be effective in various domains, especially in natural language processing and image processing. However, one of the challenges associated with using deep neural networks includes the long design time and expertise needed to apply these neural networks to a particular domain. The research presented in this paper investigates the automation of the design of the deep neural network pipeline to overcome this challenge. The deep learning pipeline includes identifying the preprocessing needed, the feature engineering technique, the neural network to use and the parameters for the neural network. A selection pertubative hyper-heuristic (SPHH) is used to automate the design pipeline. The study also examines the reusability of the generated pipeline. The effectiveness of transfer learning on the generated designs is also investigated. The proposed approach is evaluated for text processing—namely, sentiment analysis and spam detection— and image processing—namely, maize disease detection and oral lesion detection. The study revealed that the automated design of the deep neural network pipeline produces just as good, and in some cases better, performance compared to the manual design, with the automated design requiring less design time than the manual design. In the majority of instances, the design was not reusable; however, transfer learning achieved positive transfer of designs, with the performance being just as good or better than when transfer learning was not used.The National Research Foundation of South Africa.https://www.mdpi.com/journal/applsciam2023Computer Scienc

Exploiting segmentation labels and representation learning to forecast therapy response of PDAC patients

The prediction of pancreatic ductal adenocarcinoma therapy response is a clinically challenging and important task in this high-mortality tumour entity. The training of neural networks able to tackle this challenge is impeded by a lack of large datasets and the difficult anatomical localisation of the pancreas. Here, we propose a hybrid deep neural network pipeline to predict tumour response to initial chemotherapy which is based on the Response Evaluation Criteria in Solid Tumors (RECIST) score, a standardised method for cancer response evaluation by clinicians as well as tumour markers, and clinical evaluation of the patients. We leverage a combination of representation transfer from segmentation to classification, as well as localisation and representation learning. Our approach yields a remarkably data-efficient method able to predict treatment response with a ROC-AUC of 63.7% using only 477 datasets in total

Efficient Deep Image Denoising via Class Specific Convolution

Deep neural networks have been widely used in image denoising during the past few years. Even though they achieve great success on this problem, they are computationally inefficient which makes them inappropriate to be implemented in mobile devices. In this paper, we propose an efficient deep neural network for image denoising based on pixel-wise classification. Despite using a computationally efficient network cannot effectively remove the noises from any content, it is still capable to denoise from a specific type of pattern or texture. The proposed method follows such a divide and conquer scheme. We first use an efficient U-net to pixel-wisely classify pixels in the noisy image based on the local gradient statistics. Then we replace part of the convolution layers in existing denoising networks by the proposed Class Specific Convolution layers (CSConv) which use different weights for different classes of pixels. Quantitative and qualitative evaluations on public datasets demonstrate that the proposed method can reduce the computational costs without sacrificing the performance compared to state-of-the-art algorithms.Comment: The Thirty-Fifth AAAI Conference on Artificial Intelligence(AAAI-21

Fair Robust Active Learning by Joint Inconsistency

Fair Active Learning (FAL) utilized active learning techniques to achieve high model performance with limited data and to reach fairness between sensitive groups (e.g., genders). However, the impact of the adversarial attack, which is vital for various safety-critical machine learning applications, is not yet addressed in FAL. Observing this, we introduce a novel task, Fair Robust Active Learning (FRAL), integrating conventional FAL and adversarial robustness. FRAL requires ML models to leverage active learning techniques to jointly achieve equalized performance on benign data and equalized robustness against adversarial attacks between groups. In this new task, previous FAL methods generally face the problem of unbearable computational burden and ineffectiveness. Therefore, we develop a simple yet effective FRAL strategy by Joint INconsistency (JIN). To efficiently find samples that can boost the performance and robustness of disadvantaged groups for labeling, our method exploits the prediction inconsistency between benign and adversarial samples as well as between standard and robust models. Extensive experiments under diverse datasets and sensitive groups demonstrate that our method not only achieves fairer performance on benign samples but also obtains fairer robustness under white-box PGD attacks compared with existing active learning and FAL baselines. We are optimistic that FRAL would pave a new path for developing safe and robust ML research and applications such as facial attribute recognition in biometrics systems.Comment: 11 pages, 3 figure

Motion vectors and deep neural networks for video camera traps

Commercial camera traps are usually triggered by a Passive Infra-Red (PIR) motion sensor necessitating a delay between triggering and the image being captured. This often seriously limits the ability to record images of small and fast moving animals. It also results in many “empty” images, e.g., owing to moving foliage against a background of different temperature. In this paper we detail a new triggering mechanism based solely on the camera sensor. This is intended for use by citizen scientists and for deployment on an affordable, compact, low-power Raspberry Pi computer (RPi). Our system introduces a video frame filtering pipeline consisting of movement and image-based processing. This makes use of Machine Learning (ML) feasible on a live camera stream on an RPi. We describe our free and open-source software implementation of the system; introduce a suitable ecology efficiency measure that mediates between specificity and recall; provide ground-truth for a video clip collection from camera traps; and evaluate the effectiveness of our system thoroughly. Overall, our video camera trap turns out to be robust and effective