On Multi-head Ensemble of Smoothed Classifiers for Certified Robustness

Randomized Smoothing (RS) is a promising technique for certified robustness, and recently in RS the ensemble of multiple deep neural networks (DNNs) has shown state-of-the-art performances. However, such an ensemble brings heavy computation burdens in both training and certification, and yet under-exploits individual DNNs and their mutual effects, as the communication between these classifiers is commonly ignored in optimization. In this work, starting from a single DNN, we augment the network with multiple heads, each of which pertains a classifier for the ensemble. A novel training strategy, namely Self-PAced Circular-TEaching (SPACTE), is proposed accordingly. SPACTE enables a circular communication flow among those augmented heads, i.e., each head teaches its neighbor with the self-paced learning using smoothed losses, which are specifically designed in relation to certified robustness. The deployed multi-head structure and the circular-teaching scheme of SPACTE jointly contribute to diversify and enhance the classifiers in augmented heads for ensemble, leading to even stronger certified robustness than ensembling multiple DNNs (effectiveness) at the cost of much less computational expenses (efficiency), verified by extensive experiments and discussions

Automated Sleep Scoring, Deep Learning and Physician Supervision

Sleep plays a crucial role in human well-being. Polysomnography is used in sleep medicine as a diagnostic tool, so as to objectively analyze the quality of sleep. Sleep scoring is the procedure of extracting sleep cycle information from the whole-night electrophysiological signals. The scoring is done worldwide by the sleep physicians according to the official American Academy of Sleep Medicine (AASM) scoring manual. In the last decades, a wide variety of deep learning based algorithms have been proposed to automatise the sleep scoring task. In this thesis we study the reasons why these algorithms fail to be introduced in the daily clinical routine, with the perspective of bridging the existing gap between the automatic sleep scoring models and the sleep physicians. In this light, the primary step is the design of a simplified sleep scoring architecture, also providing an estimate of the model uncertainty. Beside achieving results on par with most up-to-date scoring systems, we demonstrate the efficiency of ensemble learning based algorithms, together with label smoothing techniques, in both enhancing the performance and calibrating the simplified scoring model. We introduced an uncertainty estimate procedure, so as to identify the most challenging sleep stage predictions, and to quantify the disagreement between the predictions given by the model and the annotation given by the physicians. In this thesis we also propose a novel method to integrate the inter-scorer variability into the training procedure of a sleep scoring model. We clearly show that a deep learning model is able to encode this variability, so as to better adapt to the consensus of a group of scorers-physicians. We finally address the generalization ability of a deep learning based sleep scoring system, further studying its resilience to the sleep complexity and to the AASM scoring rules. We can state that there is no need to train the algorithm strictly following the AASM guidelines. Most importantly, using data from multiple data centers results in a better performing model compared with training on a single data cohort. The variability among different scorers and data centers needs to be taken into account, more than the variability among sleep disorders

Feature based dynamic intra-video indexing

A thesis submitted in partial fulfillment for the degree of Doctor of PhilosophyWith the advent of digital imagery and its wide spread application in all vistas of life, it has become an important component in the world of communication. Video content ranging from broadcast news, sports, personal videos, surveillance, movies and entertainment and similar domains is increasing exponentially in quantity and it is becoming a challenge to retrieve content of interest from the corpora. This has led to an increased interest amongst the researchers to investigate concepts of video structure analysis, feature extraction, content annotation, tagging, video indexing, querying and retrieval to fulfil the requirements. However, most of the previous work is confined within specific domain and constrained by the quality, processing and storage capabilities. This thesis presents a novel framework agglomerating the established approaches from feature extraction to browsing in one system of content based video retrieval. The proposed framework significantly fills the gap identified while satisfying the imposed constraints of processing, storage, quality and retrieval times. The output entails a framework, methodology and prototype application to allow the user to efficiently and effectively retrieved content of interest such as age, gender and activity by specifying the relevant query. Experiments have shown plausible results with an average precision and recall of 0.91 and 0.92 respectively for face detection using Haar wavelets based approach. Precision of age ranges from 0.82 to 0.91 and recall from 0.78 to 0.84. The recognition of gender gives better precision with males (0.89) compared to females while recall gives a higher value with females (0.92). Activity of the subject has been detected using Hough transform and classified using Hiddell Markov Model. A comprehensive dataset to support similar studies has also been developed as part of the research process. A Graphical User Interface (GUI) providing a friendly and intuitive interface has been integrated into the developed system to facilitate the retrieval process. The comparison results of the intraclass correlation coefficient (ICC) shows that the performance of the system closely resembles with that of the human annotator. The performance has been optimised for time and error rate