Generative models for natural images

Nous traitons de modèles génératifs construits avec des réseaux de neurones dans le contexte de la modélisation d’images. De nos jours, trois types de modèles sont particulièrement prédominants: les modèles à variables latentes, tel que l’auto-encodeur variationnel (VAE), les modèles autorégressifs, tel que le réseau de neurones récurrent pixel (PixelRNN), et les modèles génératifs antagonistes (GANs), qui sont des modèles à transformation de bruit entrainés à l’aide d’un adversaire. Cette thèse traite de chacun de ces modèles. Le premier chapitre couvre la base des modèles génératifs, ainsi que les réseaux de neurones pro- fonds, qui constituent la technologie principalement utilisée à l’heure actuelle pour l’implémentation de modèles statistiques puissants. Dans le deuxième chapitre, nous implémentons un auto-encodeur variationnel avec un décodeur auto-régressif. Cela permet de se libérer de l’hypothèse d’indépendance des dimensions de sortie du décodeur variationnel, en modélisant une distribution jointe traçable à la place, et de doter le modèle auto-régressif d’un code latent. De plus, notre implémentation a un coût computationnel significativement réduit, si on le compare à un modèle purement auto-régressif ayant les mêmes hypothèses de modélisation et la même performance. Nous décrivons l’espace latent de façon hiérarchique, et montrons de manière qualitative la décomposition sémantique des causes latente induites par ce design. Finalement, nous présentons des résultats obtenus avec des jeux de données standards et démontrant que la performance de notre implémentation est fortement compétitive. Dans le troisième chapitre, nous présentons une procédure d’entrainement améliorée pour une variante récente de modèles génératifs antagoniste. Le «Wasserstein GAN» minimise la distance, mesurée avec la métrique de Wasserstein, entre la distribution réelle et celle générée par le modèle, ce qui le rend plus facile à entrainer qu’un GAN avec un objectif minimax. Cependant, en fonction des paramètres, il présente toujours des cas d’échecs avec certain modes d’entrainement. Nous avons découvert que le coupable est le coupage des poids, et nous le remplaçons par une pénalité sur la norme des gradients. Ceci améliore et stabilise l’entrainement, et ce sur différents types du paramètres (incluant des modèles de langue sur des données discrètes), et permet de générer des échantillons de haute qualités sur CIFAR-10 et LSUN bedrooms. Finalement, dans le quatrième chapitre, nous considérons l’usage de modèles génératifs modernes comme modèles de normalité dans un cadre de détection hors-distribution «zero-shot». Nous avons évalué certains des modèles précédemment présentés dans la thèse, et avons trouvé que les VAEs sont les plus prometteurs, bien que leurs performances laissent encore un large place à l’amélioration. Cette partie de la thèse constitue un travail en cours. Nous concluons en répétant l’importance des modèles génératifs dans le développement de l’intelligence artificielle et mentionnons quelques défis futurs.We discuss modern generative modelling of natural images based on neural networks. Three varieties of such models are particularly predominant at the time of writing: latent variable models such as variational autoencoders (VAE), autoregressive models such as pixel recurrent neural networks (PixelRNN), and generative adversarial networks (GAN), which are noise-transformation models trained with an adversary. This thesis touches on all three kinds. The first chapter covers background on generative models, along with relevant discussions about deep neural networks, which are currently the dominant technology for implementing powerful statistical models. In the second chapter, we implement variational autoencoders with autoregressive decoders. This removes the strong assumption of output dimensions being conditionally independent in variational autoencoders, instead tractably modelling a joint distribution, while also endowing autoregressive models with a latent code. Additionally, this model has significantly reduced computational cost compared to that of a purely autoregressive model with similar modelling assumptions and performance. We express the latent space as a hierarchy, and qualitatively demonstrate the semantic decomposition of latent causes induced by this design. Finally, we present results on standard datasets that demonstrate strongly competitive performance. In the third chapter, we present an improved training procedure for a recent variant on generative adversarial networks. Wasserstein GANs minimize the Earth-Mover’s distance between the real and generated distributions and have been shown to be much easier to train than with the standard minimax objective of GANs. However, they still exhibit some failure modes in training for some settings. We identify weight clipping as a culprit and replace it with a penalty on the gradient norm. This improves training further, and we demonstrate stability on a wide variety of settings (including language models over discrete data), and samples of high quality on the CIFAR-10 and LSUN bedrooms datasets. Finally, in the fourth chapter, we present work in development, where we consider the use of modern generative models as normality models in a zero-shot out-of-distribution detection setting. We evaluate some of the models we have discussed previously in the thesis, and find that VAEs are the most promising, although their overall performance leaves a lot of room for improvement. We conclude by reiterating the significance of generative modelling in the development of artificial intelligence, and mention some of the challenges ahead

Ambient awareness on a sidewalk for visually impaired

Safe navigation by avoiding obstacles is vital for visually impaired while walking on a sidewalk. There are both static and dynamic obstacles to avoid. Detection, monitoring, and estimating the threat posed by obstacles remain challenging. Also, it is imperative that the design of the system must be energy efficient and low cost. An additional challenge in designing an interactive system capable of providing useful feedback is to minimize users\u27 cognitive load. We started the development of the prototype system through classifying obstacles and providing feedback. To overcome the limitations of the classification-based system, we adopted the image annotation framework in describing the scene, which may or may not include the obstacles. Both solutions partially solved the safe navigation but were found to be ineffective in providing meaningful feedback and issues with the diurnal cycle. To address such limitations, we introduce the notion of free-path and threat level imposed by the static or dynamic obstacles. This solution reduced the overhead of obstacle detection and helped in designing meaningful feedback. Affording users a natural conversation through an interactive dialog enabled interface was found to promote safer navigation. In this dissertation, we modeled the free-path and threat level using a reinforcement learning (RL) framework.We built the RL model in the Gazebo robot simulation environment and implanted that in a handheld device. A natural conversation model was created using data collected through a Wizard of OZ approach. The RL model and conversational agent model together resulted in the handheld assistive device called Augmented Guiding Torch (AGT). The AGT provides improved mobility over white cane by providing ambient awareness through natural conversation. It can inform the visually impaired about the obstacles which are helpful to be warned about ahead of time, e.g., construction site, scooter, crowd, car, bike, or big hole. Using the RL framework, the robot avoided over 95% obstacles. The visually impaired avoided over 85% obstacles with the help of AGT on a 500 feet U-shape sidewalk. Findings of this dissertation support the effectiveness of augmented guiding through RL for navigation and obstacle avoidance of visually impaired users

Improving predictive behavior under distributional shift

L'hypothèse fondamentale guidant la pratique de l'apprentissage automatique est qu’en phase de test, les données sont \emph{indépendantes et identiquement distribuées} à la distribution d'apprentissage. En pratique, les ensembles d'entraînement sont souvent assez petits pour favoriser le recours à des biais trompeurs. De plus, lorsqu'il est déployé dans le monde réel, un modèle est susceptible de rencontrer des données nouvelles ou anormales. Lorsque cela se produit, nous aimerions que nos modèles communiquent une confiance prédictive réduite. De telles situations, résultant de différentes formes de changement de distribution, sont incluses dans ce que l'on appelle actuellement les situations \emph{hors distribution} (OOD). Dans cette thèse par article, nous discutons des aspects de performance OOD relativement à des changement de distribution sémantique et non sémantique -- ceux-ci correspondent à des instances de détection OOD et à des problèmes de généralisation OOD. Dans le premier article, nous évaluons de manière critique le problème de la détection OOD, en se concentrant sur l’analyse comparative et l'évaluation. Tout en soutenant que la détection OOD est trop vague pour être significative, nous suggérons plutôt de détecter les anomalies sémantiques. Nous montrons que les classificateurs entraînés sur des objectifs auxiliaires auto-supervisés peuvent améliorer la sémanticité dans les représentations de caractéristiques, comme l’indiquent notre meilleure détection des anomalies sémantiques ainsi que notre meilleure généralisation. Dans le deuxième article, nous développons davantage notre discussion sur le double objectif de robustesse au changement de distribution non sémantique et de sensibilité au changement sémantique. Adoptant une perspective de compositionnalité, nous décomposons le changement non sémantique en composants systématiques et non systématiques, la généralisation en distribution et la détection d'anomalies sémantiques formant les tâches correspondant à des compositions complémentaires. Nous montrons au moyen d'évaluations empiriques sur des tâches synthétiques qu'il est possible d'améliorer simultanément les performances sur tous ces aspects de robustesse et d'incertitude. Nous proposons également une méthode simple qui améliore les approches existantes sur nos tâches synthétiques. Dans le troisième et dernier article, nous considérons un scénario de boîte noire en ligne dans lequel non seulement la distribution des données d'entrée conditionnées sur les étiquettes change de l’entraînement au test, mais aussi la distribution marginale des étiquettes. Nous montrons que sous de telles contraintes pratiques, de simples estimations probabilistes en ligne du changement d'étiquette peuvent quand même être une piste prometteuse. Nous terminons par une brève discussion sur les pistes possibles.The fundamental assumption guiding practice in machine learning has been that test-time data is \emph{independent and identically distributed} to the training distribution. In practical use, training sets are often small enough to encourage reliance upon misleading biases. Additionally, when deployed in the real-world, a model is likely to encounter novel or anomalous data. When this happens, we would like our models to communicate reduced predictive confidence. Such situations, arising as a result of different forms of distributional shift, comprise what are currently termed \emph{out-of-distribution} (OOD) settings. In this thesis-by-article, we discuss aspects of OOD performance with regards to semantic and non-semantic distributional shift — these correspond to instances of OOD detection and OOD generalization problems. In the first article, we critically appraise the problem of OOD detection, with regard to benchmarking and evaluation. Arguing that OOD detection is too broad to be meaningful, we suggest detecting semantic anomalies instead. We show that classifiers trained with auxiliary self-supervised objectives can improve semanticity in feature representations, as indicated by improved semantic anomaly detection as well as improved generalization. In the second article, we further develop our discussion of the twin goals of robustness to non-semantic distributional shift and sensitivity to semantic shift. Adopting a perspective of compositionality, we decompose non-semantic shift into systematic and non-systematic components, along with in-distribution generalization and semantic anomaly detection forming the complementary tasks. We show by means of empirical evaluations on synthetic setups that it is possible to improve performance at all these aspects of robustness and uncertainty simultaneously. We also propose a simple method that improves upon existing approaches on our synthetic benchmarks. In the third and final article, we consider an online, black-box scenario in which both the distribution of input data conditioned on labels changes from training to testing, as well as the marginal distribution of labels. We show that under such practical constraints, simple online probabilistic estimates of label-shift can nevertheless be a promising approach. We close with a brief discussion of possible avenues forward

A Doubly-Fed Induction Generator (DFIG)-Based Wind-Power System with Integrated Energy Storage for Remote Electrification

Electrification of off-grid remote communities is commonly accomplished through diesel generators. The method may even be employed in cases where there exists an un­ reliable connection to the power grid. Regardless, the method is environmentally-hostile, typically costly, and likely risky. Therefore, to mitigate the reliance on diesel fuel, uti­ lization of renewable energy resources has been considered in recent years. This thesis investigates the feasibility of and technical considerations involved in the employment of a specific class of variable-speed wind-power systems, integrated with battery energy stor­ age, for remote electrification applications. The wind-power system under consideration is based on the doubly-fed induction gen­ erator (DFIG) technology, which features a number of characteristics that render it at­ tractive for the incorporation of battery energy storage. This thesis identifies the control strategy, different control sub-functions, and the controllers structures/parametes required to accommodate the battery energy storage. The developed control strategy enables the operation of the wind-power/storage system in the off-grid (islanded) mode of operation, as well as the grid-connected mode of operation. Under the developed control strategy, the wind-power/storage system can operate in parallel with constant-speed wind-power units, passive loads, and induction motor loads. The effectiveness of the proposed control strategy has been demonstrated through comprehensive simulation studies enabled by the commercial software package PSCAD/EMTDC. In addition to the control aspects, this thesis studies the reliability aspects of the pro­ posed wind-power/storage system, for an example remote electrification system. Thus, a new reliability assessment method has been developed in this thesis, which combines the existing analytical and simulation-based probabilistic approaches. The reliability analysis conducted indicates that the battery energy storage capacity, the wind magnitude and pro­ file, and the load profile impose remarkable impacts on the reliability of the electrification system. It also indicates that a connection to the power grid, however unreliable, signifi­ cantly mitigates the need for a large battery to achieve a given degree of reliability

Detecting semantic anomalies

We critically appraise the recent interest in out-of-distribution (OOD) detection and question the practical relevance of existing benchmarks. While the currently prevalent trend is to consider different datasets as OOD, we argue that out-distributions of practical interest are ones where the distinction is semantic in nature for a specified context, and that evaluative tasks should reflect this more closely. Assuming a context of object recognition, we recommend a set of benchmarks, motivated by practical applications. We make progress on these benchmarks by exploring a multi-task learning based approach, showing that auxiliary objectives for improved semantic awareness result in improved semantic anomaly detection, with accompanying generalization benefits.Comment: Preprint for AAAI '20 publicatio

Factors associated with stress among first-year undergraduate students attending an Australian university

Objective: The aim of this study was to examine the relationship between stress and various socio-demographic, health and behavioural factors among undergraduate students studying in an Australian university. Methods: A cross-sectional survey was carried out among firstyear undergraduate students studying at Griffith University. Participants were recruited from four different academic groups (N=728). The questionnaire used in this study comprised of three sections: socio-demographic information, stress scale and a food frequency questionnaire. K-means Cluster analysis was performed to identify the major dietary patterns and multinomial logistic regression analysis was used to examine the factors associated with stress. Results: Nearly 53% of the students had some degree of stress with 37.4% experiencing moderate to severe levels of stress. The factors most strongly associated with having mild or moderate/ severe stress levels included being in a relationship [OR =1.71, 95% CI (1.02-2.87) and OR=1.61, 95% CI (1.06-2.44)], studying a non-health related degree [OR=1.68, 95% CI (1.03-2.73) and OR=1.51, 95% CI (1.04-2.19)], working ≥ 21 hours per week [OR=2.12, 95% CI (1.02-4.40) and OR=2.21, 95% CI (1.32-3.67)], and engaging in an unhealthy dietary pattern [OR=2.67, 95% CI (1.25-5.72) and OR=2.76, 95% CI (1.47-5.16)]. Being a female [OR=1.84, 95% CI (1.25-2.72)], living in a shared accommodation [OR=0.52, 95% CI (0.27-0.98)], rarely exercising [OR=2.64, 95% CI (1.59-4.39)], having a body mass index (BMI) of 25 or over [OR=2.03, 95% CI (1.36-3.04)], and engaging in a dietary pattern that was low in protein, fruit and vegetables [OR=1.72, 95% CI (1.06-2.77)] were also associated with having moderate/severe stress levels. Conclusion: This study found that more than half of the undergraduate students had some levels of stress. Both mild and moderate/severe levels of stress were associated with sociodemographic characteristics, risky health behaviours and poor dietary patterns. Our findings reinforce the need to promote healthy behaviours among undergraduate university students in order to maintain good mental health.</p