233 research outputs found

    Online Multi-Stage Deep Architectures for Feature Extraction and Object Recognition

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    Multi-stage visual architectures have recently found success in achieving high classification accuracies over image datasets with large variations in pose, lighting, and scale. Inspired by techniques currently at the forefront of deep learning, such architectures are typically composed of one or more layers of preprocessing, feature encoding, and pooling to extract features from raw images. Training these components traditionally relies on large sets of patches that are extracted from a potentially large image dataset. In this context, high-dimensional feature space representations are often helpful for obtaining the best classification performances and providing a higher degree of invariance to object transformations. Large datasets with high-dimensional features complicate the implementation of visual architectures in memory constrained environments. This dissertation constructs online learning replacements for the components within a multi-stage architecture and demonstrates that the proposed replacements (namely fuzzy competitive clustering, an incremental covariance estimator, and multi-layer neural network) can offer performance competitive with their offline batch counterparts while providing a reduced memory footprint. The online nature of this solution allows for the development of a method for adjusting parameters within the architecture via stochastic gradient descent. Testing over multiple datasets shows the potential benefits of this methodology when appropriate priors on the initial parameters are unknown. Alternatives to batch based decompositions for a whitening preprocessing stage which take advantage of natural image statistics and allow simple dictionary learners to work well in the problem domain are also explored. Expansions of the architecture using additional pooling statistics and multiple layers are presented and indicate that larger codebook sizes are not the only step forward to higher classification accuracies. Experimental results from these expansions further indicate the important role of sparsity and appropriate encodings within multi-stage visual feature extraction architectures

    Multi-Label Latent Spaces with Semi-Supervised Deep Generative Models

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    Expert labeling, tagging, and assessment are far more costly than the processes of collecting raw data. Generative modeling is a very powerful tool to tackle this real-world problem. It is shown here how these models can be used to allow for semi-supervised learning that performs very well in label-deficient conditions. The foundation for the work in this dissertation is built upon visualizing generative models\u27 latent spaces to gain deeper understanding of data, analyze faults, and propose solutions. A number of novel ideas and approaches are presented to improve single-label classification. This dissertation\u27s main focus is on extending semi-supervised Deep Generative Models for solving the multi-label problem by proposing unique mathematical and programming concepts and organization. In all naive mixtures, using multiple labels is detrimental and causes each label\u27s predictions to be worse than models that utilize only a single label. Examining latent spaces reveals that in many cases, large regions in the models generate meaningless results. Enforcing a priori independence is essential, and only when applied can multi-label models outperform the best single-label models. Finally, a novel learning technique called open-book learning is described that is capable of surpassing the state-of-the-art classification performance of generative models for multi-labeled, semi-supervised data sets

    Learning from minimally labeled data with accelerated convolutional neural networks

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    The main objective of an Artificial Vision Algorithm is to design a mapping function that takes an image as an input and correctly classifies it into one of the user-determined categories. There are several important properties to be satisfied by the mapping function for visual understanding. First, the function should produce good representations of the visual world, which will be able to recognize images independently of pose, scale and illumination. Furthermore, the designed artificial vision system has to learn these representations by itself. Recent studies on Convolutional Neural Networks (ConvNets) produced promising advancements in visual understanding. These networks attain significant performance upgrades by relying on hierarchical structures inspired by biological vision systems. In my research, I work mainly in two areas: 1) how ConvNets can be programmed to learn the optimal mapping function using the minimum amount of labeled data, and 2) how these networks can be accelerated for practical purposes. In this work, algorithms that learn from unlabeled data are studied. A new framework that exploits unlabeled data is proposed. The proposed framework obtains state-of-the-art performance results in different tasks. Furthermore, this study presents an optimized streaming method for ConvNets’ hardware accelerator on an embedded platform. It is tested on object classification and detection applications using ConvNets. Experimental results indicate high computational efficiency, and significant performance upgrades over all other existing platforms

    Apprentissage automatique pour le codage cognitif de la parole

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    Depuis les années 80, les codecs vocaux reposent sur des stratégies de codage à court terme qui fonctionnent au niveau de la sous-trame ou de la trame (généralement 5 à 20 ms). Les chercheurs ont essentiellement ajusté et combiné un nombre limité de technologies disponibles (transformation, prédiction linéaire, quantification) et de stratégies (suivi de forme d'onde, mise en forme du bruit) pour construire des architectures de codage de plus en plus complexes. Dans cette thèse, plutôt que de s'appuyer sur des stratégies de codage à court terme, nous développons un cadre alternatif pour la compression de la parole en codant les attributs de la parole qui sont des caractéristiques perceptuellement importantes des signaux vocaux. Afin d'atteindre cet objectif, nous résolvons trois problèmes de complexité croissante, à savoir la classification, la prédiction et l'apprentissage des représentations. La classification est un élément courant dans les conceptions de codecs modernes. Dans un premier temps, nous concevons un classifieur pour identifier les émotions, qui sont parmi les attributs à long terme les plus complexes de la parole. Dans une deuxième étape, nous concevons un prédicteur d'échantillon de parole, qui est un autre élément commun dans les conceptions de codecs modernes, pour mettre en évidence les avantages du traitement du signal de parole à long terme et non linéaire. Ensuite, nous explorons les variables latentes, un espace de représentations de la parole, pour coder les attributs de la parole à court et à long terme. Enfin, nous proposons un réseau décodeur pour synthétiser les signaux de parole à partir de ces représentations, ce qui constitue notre dernière étape vers la construction d'une méthode complète de compression de la parole basée sur l'apprentissage automatique de bout en bout. Bien que chaque étape de développement proposée dans cette thèse puisse faire partie d'un codec à elle seule, chaque étape fournit également des informations et une base pour la prochaine étape de développement jusqu'à ce qu'un codec entièrement basé sur l'apprentissage automatique soit atteint. Les deux premières étapes, la classification et la prédiction, fournissent de nouveaux outils qui pourraient remplacer et améliorer des éléments des codecs existants. Dans la première étape, nous utilisons une combinaison de modèle source-filtre et de machine à état liquide (LSM), pour démontrer que les caractéristiques liées aux émotions peuvent être facilement extraites et classées à l'aide d'un simple classificateur. Dans la deuxième étape, un seul réseau de bout en bout utilisant une longue mémoire à court terme (LSTM) est utilisé pour produire des trames vocales avec une qualité subjective élevée pour les applications de masquage de perte de paquets (PLC). Dans les dernières étapes, nous nous appuyons sur les résultats des étapes précédentes pour concevoir un codec entièrement basé sur l'apprentissage automatique. un réseau d'encodage, formulé à l'aide d'un réseau neuronal profond (DNN) et entraîné sur plusieurs bases de données publiques, extrait et encode les représentations de la parole en utilisant la prédiction dans un espace latent. Une approche d'apprentissage non supervisé basée sur plusieurs principes de cognition est proposée pour extraire des représentations à partir de trames de parole courtes et longues en utilisant l'information mutuelle et la perte contrastive. La capacité de ces représentations apprises à capturer divers attributs de la parole à court et à long terme est démontrée. Enfin, une structure de décodage est proposée pour synthétiser des signaux de parole à partir de ces représentations. L'entraînement contradictoire est utilisé comme une approximation des mesures subjectives de la qualité de la parole afin de synthétiser des échantillons de parole à consonance naturelle. La haute qualité perceptuelle de la parole synthétisée ainsi obtenue prouve que les représentations extraites sont efficaces pour préserver toutes sortes d'attributs de la parole et donc qu'une méthode de compression complète est démontrée avec l'approche proposée.Abstract: Since the 80s, speech codecs have relied on short-term coding strategies that operate at the subframe or frame level (typically 5 to 20ms). Researchers essentially adjusted and combined a limited number of available technologies (transform, linear prediction, quantization) and strategies (waveform matching, noise shaping) to build increasingly complex coding architectures. In this thesis, rather than relying on short-term coding strategies, we develop an alternative framework for speech compression by encoding speech attributes that are perceptually important characteristics of speech signals. In order to achieve this objective, we solve three problems of increasing complexity, namely classification, prediction and representation learning. Classification is a common element in modern codec designs. In a first step, we design a classifier to identify emotions, which are among the most complex long-term speech attributes. In a second step, we design a speech sample predictor, which is another common element in modern codec designs, to highlight the benefits of long-term and non-linear speech signal processing. Then, we explore latent variables, a space of speech representations, to encode both short-term and long-term speech attributes. Lastly, we propose a decoder network to synthesize speech signals from these representations, which constitutes our final step towards building a complete, end-to-end machine-learning based speech compression method. The first two steps, classification and prediction, provide new tools that could replace and improve elements of existing codecs. In the first step, we use a combination of source-filter model and liquid state machine (LSM), to demonstrate that features related to emotions can be easily extracted and classified using a simple classifier. In the second step, a single end-to-end network using long short-term memory (LSTM) is shown to produce speech frames with high subjective quality for packet loss concealment (PLC) applications. In the last steps, we build upon the results of previous steps to design a fully machine learning-based codec. An encoder network, formulated using a deep neural network (DNN) and trained on multiple public databases, extracts and encodes speech representations using prediction in a latent space. An unsupervised learning approach based on several principles of cognition is proposed to extract representations from both short and long frames of data using mutual information and contrastive loss. The ability of these learned representations to capture various short- and long-term speech attributes is demonstrated. Finally, a decoder structure is proposed to synthesize speech signals from these representations. Adversarial training is used as an approximation to subjective speech quality measures in order to synthesize natural-sounding speech samples. The high perceptual quality of synthesized speech thus achieved proves that the extracted representations are efficient at preserving all sorts of speech attributes and therefore that a complete compression method is demonstrated with the proposed approach

    Data Visualization, Dimensionality Reduction, and Data Alignment via Manifold Learning

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    The high dimensionality of modern data introduces significant challenges in descriptive and exploratory data analysis. These challenges gave rise to extensive work on dimensionality reduction and manifold learning aiming to provide low dimensional representations that preserve or uncover intrinsic patterns and structures in the data. In this thesis, we expand the current literature in manifold learning developing two methods called DIG (Dynamical Information Geometry) and GRAE (Geometry Regularized Autoencoders). DIG is a method capable of finding low-dimensional representations of high-frequency multivariate time series data, especially suited for visualization. GRAE is a general framework which splices the well-established machinery from kernel manifold learning methods to recover a sensitive geometry, alongside the parametric structure of autoencoders. Manifold learning can also be useful to study data collected from different measurement instruments, conditions, or protocols of the same underlying system. In such cases the data is acquired in a multi-domain representation. The last two Chapters of this thesis are devoted to two new methods capable of aligning multi-domain data, leveraging their geometric structure alongside limited common information. First, we present DTA (Diffusion Transport Alignment), a semi-supervised manifold alignment method that exploits prior one-to-one correspondence knowledge between distinct data views and finds an aligned common representation. And finally, we introduce MALI (Manifold Alignment with Label Information). Here we drop the one-to-one prior correspondences assumption, since in many scenarios such information can not be provided, either due to the nature of the experimental design, or it becomes extremely costly. Instead, MALI only needs side-information in the form of discrete labels/classes present in both domains

    Evaluation and implementation of an auto-encoder for compression of satellite images in the ScOSA project

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    The thesis evaluates the efficiency of various autoencoder neural networks for image compression regarding satellite imagery. The results highlight the evaluation and implementation of autoencoder architectures and the procedures required to deploy neural networks to reliable embedded devices. The developed autoencoders evaluated, targeting a ZYNQ 7020 FPGA (Field Programmable Gate Array) and a ZU7EV FPGA

    A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks

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    Transformer is a deep neural network that employs a self-attention mechanism to comprehend the contextual relationships within sequential data. Unlike conventional neural networks or updated versions of Recurrent Neural Networks (RNNs) such as Long Short-Term Memory (LSTM), transformer models excel in handling long dependencies between input sequence elements and enable parallel processing. As a result, transformer-based models have attracted substantial interest among researchers in the field of artificial intelligence. This can be attributed to their immense potential and remarkable achievements, not only in Natural Language Processing (NLP) tasks but also in a wide range of domains, including computer vision, audio and speech processing, healthcare, and the Internet of Things (IoT). Although several survey papers have been published highlighting the transformer's contributions in specific fields, architectural differences, or performance evaluations, there is still a significant absence of a comprehensive survey paper encompassing its major applications across various domains. Therefore, we undertook the task of filling this gap by conducting an extensive survey of proposed transformer models from 2017 to 2022. Our survey encompasses the identification of the top five application domains for transformer-based models, namely: NLP, Computer Vision, Multi-Modality, Audio and Speech Processing, and Signal Processing. We analyze the impact of highly influential transformer-based models in these domains and subsequently classify them based on their respective tasks using a proposed taxonomy. Our aim is to shed light on the existing potential and future possibilities of transformers for enthusiastic researchers, thus contributing to the broader understanding of this groundbreaking technology

    Evaluation and implementation of an auto-encoder for compression of satellite images in the ScOSA project

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    The thesis evaluates the efficiency of various autoencoder neural networks for image compression regarding satellite imagery. The results highlight the evaluation and implementation of autoencoder architectures and the procedures required to deploy neural networks to reliable embedded devices. The developed autoencoders evaluated, targeting a ZYNQ 7020 FPGA (Field Programmable Gate Array) and a ZU7EV FPGA

    Deep learning in food category recognition

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    Integrating artificial intelligence with food category recognition has been a field of interest for research for the past few decades. It is potentially one of the next steps in revolutionizing human interaction with food. The modern advent of big data and the development of data-oriented fields like deep learning have provided advancements in food category recognition. With increasing computational power and ever-larger food datasets, the approach’s potential has yet to be realized. This survey provides an overview of methods that can be applied to various food category recognition tasks, including detecting type, ingredients, quality, and quantity. We survey the core components for constructing a machine learning system for food category recognition, including datasets, data augmentation, hand-crafted feature extraction, and machine learning algorithms. We place a particular focus on the field of deep learning, including the utilization of convolutional neural networks, transfer learning, and semi-supervised learning. We provide an overview of relevant studies to promote further developments in food category recognition for research and industrial applicationsMRC (MC_PC_17171)Royal Society (RP202G0230)BHF (AA/18/3/34220)Hope Foundation for Cancer Research (RM60G0680)GCRF (P202PF11)Sino-UK Industrial Fund (RP202G0289)LIAS (P202ED10Data Science Enhancement Fund (P202RE237)Fight for Sight (24NN201);Sino-UK Education Fund (OP202006)BBSRC (RM32G0178B8
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