Academic literature on the topic 'Réseaux convolutionnels'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Contents
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Réseaux convolutionnels.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Dissertations / Theses on the topic "Réseaux convolutionnels"
Bietti, Alberto. "Méthodes à noyaux pour les réseaux convolutionnels profonds." Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAM051.
Full textThe increased availability of large amounts of data, from images in social networks, speech waveforms from mobile devices, and large text corpuses, to genomic and medical data, has led to a surge of machine learning techniques. Such methods exploit statistical patterns in these large datasets for making accurate predictions on new data. In recent years, deep learning systems have emerged as a remarkably successful class of machine learning algorithms, which rely on gradient-based methods for training multi-layer models that process data in a hierarchical manner. These methods have been particularly successful in tasks where the data consists of natural signals such as images or audio; this includes visual recognition, object detection or segmentation, and speech recognition.For such tasks, deep learning methods often yield the best known empirical performance; yet, the high dimensionality of the data and large number of parameters of these models make them challenging to understand theoretically. Their success is often attributed in part to their ability to exploit useful structure in natural signals, such as local stationarity or invariance, for instance through choices of network architectures with convolution and pooling operations. However, such properties are still poorly understood from a theoretical standpoint, leading to a growing gap between the theory and practice of machine learning. This thesis is aimed towards bridging this gap, by studying spaces of functions which arise from given network architectures, with a focus on the convolutional case. Our study relies on kernel methods, by considering reproducing kernel Hilbert spaces (RKHSs) associated to certain kernels that are constructed hierarchically based on a given architecture. This allows us to precisely study smoothness, invariance, stability to deformations, and approximation properties of functions in the RKHS. These representation properties are also linked with optimization questions when training deep networks with gradient methods in some over-parameterized regimes where such kernels arise. They also suggest new practical regularization strategies for obtaining better generalization performance on small datasets, and state-of-the-art performance for adversarial robustness on image tasks
Ogier, du Terrail Jean. "Réseaux de neurones convolutionnels profonds pour la détection de petits véhicules en imagerie aérienne." Thesis, Normandie, 2018. http://www.theses.fr/2018NORMC276/document.
Full textThe following manuscript is an attempt to tackle the problem of small vehicles detection in vertical aerial imagery through the use of deep learning algorithms. The specificities of the matter allows the use of innovative techniques leveraging the invariance and self similarities of automobiles/planes vehicles seen from the sky.We will start by a thorough study of single shot detectors. Building on that we will examine the effect of adding multiple stages to the detection decision process. Finally we will try to come to grips with the domain adaptation problem in detection through the generation of better looking synthetic data and its use in the training process of these detectors
Morvan, Ludivine. "Prédiction de la progression du myélome multiple par imagerie TEP : Adaptation des forêts de survie aléatoires et de réseaux de neurones convolutionnels." Thesis, Ecole centrale de Nantes, 2021. http://www.theses.fr/2021ECDN0045.
Full textThe aim of this work is to provide a model for survival prediction and biomarker identification in the context of multiple myeloma (MM) using PET (Positron Emission Tomography) imaging and clinical data. This PhD is divided into two parts: The first part provides a model based on Random Survival Forests (RSF). The second part is based on the adaptation of deep learning to survival and to our data. The main contributions are the following: 1) Production of a model based on RSF and PET images allowing the prediction of a risk group for multiple myeloma patients. 2) Determination of biomarkers using this model.3) Demonstration of the interest of PET radiomics.4) Extension of the state of the art of methods for the adaptation of deep learning to a small database and small images. 5) Study of the cost functions used in survival. In addition, we are, to our knowledge, the first to investigate the use of RSFs in the context of MM and PET images, to use self-supervised pre-training with PET images, and, with a survival task, to fit the triplet cost function to survival and to fit a convolutional neural network to MM survival from PET lesions
Guedria, Soulaimane. "Une plateforme d'apprentissage profond à base de composants qui passe à l'échelle : une application aux réseaux de neurones convolutionnels pour la segmentation en imagerie médicale." Thesis, Université Grenoble Alpes, 2020. http://www.theses.fr/2020GRALM023.
Full textDeep neural networks (DNNs) and particularly convolutional neural networks (CNNs) trained on large datasets are getting great success across a plethora of paramount applications. It has been providing powerful solutions and revolutionizing medicine, particularly, in the medical image analysis field. However, deep learning field comes up with multiple challenges: (1) training Convolutional Neural Networks (CNNs) is a computationally intensive and time-consuming task (2) introducing parallelism to CNNs in practice as it is a tedious, repetitive and error-prone process and (3) there is currently no broad study of the generalizability and the reproducibility of the CNN parallelism techniques on concrete medical imaging segmentation applications.Within this context, the present PhD thesis aims to tackle the aforementioned challenges. To achieve this goal, we conceived, implemented and validated an all-in-one scalable and component-based deep learning parallelism platform for medical imaging segmentation. First, we introduce R2D2, an end-to-end scalable deep learning toolkit for medical imaging segmentation. R2D2 proposes a set of new distributed versions of widely-used deep learning architectures (FCN and U-Net) in order to speed up building new distributive deep learning models and reduce the gap between researchers and talent-intensive deep learning. Next, this thesis also introduces Auto-CNNp, a component-based software framework to automate CNN parallelism throughout encapsulating and hiding typical CNNs parallelization routine tasks within a backbone structure while being extensible for user-specific customization. The evaluation results of our proposed automated component-based approach are promising. It shows that a significant speedup in the CNN parallelization task has been achieved to the detriment of a negligible framework execution time, compared to the manual parallelization strategy. The previously introduced couple of software solutions (R2D2 and Auto-CNNp) at our disposal led us to conduct a thorough and practical analysis of the generalizability of the CNN parallelism techniques to the imaging segmentation applications. Concurrently, we perform an in-depth literature review aiming to identify the sources of variability and study reproducibility issues of deep learning training process for particular CNNs training configurations applied for medical imaging segmentation. We also draw a set of good practices recommendations aiming to alleviate the aforementioned reproducibility issues for medical imaging segmentation DNNs training process. Finally, we make a number of observations based on a broad analysis of the results of the already conducted CNN parallelism experimental study which led us to propose a guideline and recommendations for scaling up CNNs for segmentation applications. We succeeded to eliminate the accuracy loss with scale for the U-Net CNN architecture and alleviate the accuracy degradation for the FCN CNN architecture
Douillard, Arthur. "Continual Learning for Computer Vision." Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS165.
Full textI first review the existing methods based on regularization for continual learning. While regularizing a model's probabilities is very efficient to reduce forgetting in large-scale datasets, there are few works considering constraints on intermediate features. I cover in this chapter two contributions aiming to regularize directly the latent space of ConvNet. The first one, PODNet, aims to reduce the drift of spatial statistics between the old and new model, which in effect reduces drastically forgetting of old classes while enabling efficient learning of new classes. I show in a second part a complementary method where we avoid pre-emptively forgetting by allocating locations in the latent space for yet unseen future class. Then, I describe a recent application of CIL to semantic segmentation. I show that the very nature of CSS offer new specific challenges, namely forgetting on large images and a background shift. We tackle the first problem by extending our distillation loss introduced in the previous chapter to multi-scales. The second problem is solved by an efficient pseudo-labeling strategy. Finally, we consider the common rehearsal learning, but applied this time to CSS. I show that it cannot be used naively because of memory complexity and design a light-weight rehearsal that is even more efficient. Finally, I consider a completely different approach to continual learning: dynamic networks where the parameters are extended during training to adapt to new tasks. Previous works on this domain are hard to train and often suffer from parameter count explosion. For the first time in continual computer vision, we propose to use the Transformer architecture: the model dimension mostly fixed and shared across tasks, except for an expansion of learned task tokens. With an encoder/decoder strategy where the decoder forward is specialized by a task token, we show state-of-the-art robustness to forgetting while our memory and computational complexities barely grow
Chevalier, Marion. "Résolution variable et information privilégiée pour la reconnaissance d'images." Thesis, Paris 6, 2016. http://www.theses.fr/2016PA066726/document.
Full textImage classification has a prominent interest in numerous visual recognition tasks, particularly for vehicle recognition in airborne systems, where the images have a low resolution because of the large distance between the system and the observed scene. During the training phase, complementary data such as knowledge on the position of the system or high-resolution images may be available. In our work, we focus on the task of low-resolution image classification while taking into account supplementary information during the training phase. We first show the interest of deep convolutional networks for the low-resolution image recognition, especially by proposing an architecture learned on the targeted data. On the other hand, we rely on the framework of learning using privileged information to benefit from the complementary training data, here the high-resolution versions of the images. We propose two novel methods for integrating privileged information in the learning phase of neural networks. Our first model relies on these complementary data to compute an absolute difficulty level, assigning a large weight to the most easily recognized images. Our second model introduces a similarity constraint between the networks learned on each type of data. We experimentally validate our models on several application cases, especially in a fine-grained oriented context and on a dataset containing annotation noise
Saxena, Shreyas. "Apprentissage de représentations pour la reconnaissance visuelle." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAM080/document.
Full textIn this dissertation, we propose methods and data driven machine learning solutions which address and benefit from the recent overwhelming growth of digital media content.First, we consider the problem of improving the efficiency of image retrieval. We propose a coordinated local metric learning (CLML) approach which learns local Mahalanobis metrics, and integrates them in a global representation where the l2 distance can be used. This allows for data visualization in a single view, and use of efficient ` 2 -based retrieval methods. Our approach can be interpreted as learning a linear projection on top of an explicit high-dimensional embedding of a kernel. This interpretation allows for the use of existing frameworks for Mahalanobis metric learning for learning local metrics in a coordinated manner. Our experiments show that CLML improves over previous global and local metric learning approaches for the task of face retrieval.Second, we present an approach to leverage the success of CNN models forvisible spectrum face recognition to improve heterogeneous face recognition, e.g., recognition of near-infrared images from visible spectrum training images. We explore different metric learning strategies over features from the intermediate layers of the networks, to reduce the discrepancies between the different modalities. In our experiments we found that the depth of the optimal features for a given modality, is positively correlated with the domain shift between the source domain (CNN training data) and the target domain. Experimental results show the that we can use CNNs trained on visible spectrum images to obtain results that improve over the state-of-the art for heterogeneous face recognition with near-infrared images and sketches.Third, we present convolutional neural fabrics for exploring the discrete andexponentially large CNN architecture space in an efficient and systematic manner. Instead of aiming to select a single optimal architecture, we propose a “fabric” that embeds an exponentially large number of architectures. The fabric consists of a 3D trellis that connects response maps at different layers, scales, and channels with a sparse homogeneous local connectivity pattern. The only hyperparameters of the fabric (the number of channels and layers) are not critical for performance. The acyclic nature of the fabric allows us to use backpropagation for learning. Learning can thus efficiently configure the fabric to implement each one of exponentially many architectures and, more generally, ensembles of all of them. While scaling linearly in terms of computation and memory requirements, the fabric leverages exponentially many chain-structured architectures in parallel by massively sharing weights between them. We present benchmark results competitive with the state of the art for image classification on MNIST and CIFAR10, and for semantic segmentation on the Part Labels dataset
Tang, Yuxing. "Weakly supervised learning of deformable part models and convolutional neural networks for object detection." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSEC062/document.
Full textIn this dissertation we address the problem of weakly supervised object detection, wherein the goal is to recognize and localize objects in weakly-labeled images where object-level annotations are incomplete during training. To this end, we propose two methods which learn two different models for the objects of interest. In our first method, we propose a model enhancing the weakly supervised Deformable Part-based Models (DPMs) by emphasizing the importance of location and size of the initial class-specific root filter. We first compute a candidate pool that represents the potential locations of the object as this root filter estimate, by exploring the generic objectness measurement (region proposals) to combine the most salient regions and “good” region proposals. We then propose learning of the latent class label of each candidate window as a binary classification problem, by training category-specific classifiers used to coarsely classify a candidate window into either a target object or a non-target class. Furthermore, we improve detection by incorporating the contextual information from image classification scores. Finally, we design a flexible enlarging-and-shrinking post-processing procedure to modify the DPMs outputs, which can effectively match the approximate object aspect ratios and further improve final accuracy. Second, we investigate how knowledge about object similarities from both visual and semantic domains can be transferred to adapt an image classifier to an object detector in a semi-supervised setting on a large-scale database, where a subset of object categories are annotated with bounding boxes. We propose to transform deep Convolutional Neural Networks (CNN)-based image-level classifiers into object detectors by modeling the differences between the two on categories with both image-level and bounding box annotations, and transferring this information to convert classifiers to detectors for categories without bounding box annotations. We have evaluated both our approaches extensively on several challenging detection benchmarks, e.g. , PASCAL VOC, ImageNet ILSVRC and Microsoft COCO. Both our approaches compare favorably to the state-of-the-art and show significant improvement over several other recent weakly supervised detection methods
Tsogkas, Stavros. "Mid-level representations for modeling objects." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLC012/document.
Full textIn this thesis we propose the use of mid-level representations, and in particular i) medial axes, ii) object parts, and iii)convolutional features, for modelling objects.The first part of the thesis deals with detecting medial axes in natural RGB images. We adopt a learning approach, utilizing colour, texture and spectral clustering features, to build a classifier that produces a dense probability map for symmetry. Multiple Instance Learning (MIL) allows us to treat scale and orientation as latent variables during training, while a variation based on random forests offers significant gains in terms of running time.In the second part of the thesis we focus on object part modeling using both hand-crafted and learned feature representations. We develop a coarse-to-fine, hierarchical approach that uses probabilistic bounds for part scores to decrease the computational cost of mixture models with a large number of HOG-based templates. These efficiently computed probabilistic bounds allow us to quickly discard large parts of the image, and evaluate the exact convolution scores only at promising locations. Our approach achieves a $4times-5times$ speedup over the naive approach with minimal loss in performance.We also employ convolutional features to improve object detection. We use a popular CNN architecture to extract responses from an intermediate convolutional layer. We integrate these responses in the classic DPM pipeline, replacing hand-crafted HOG features, and observe a significant boost in detection performance (~14.5% increase in mAP).In the last part of the thesis we experiment with fully convolutional neural networks for the segmentation of object parts.We re-purpose a state-of-the-art CNN to perform fine-grained semantic segmentation of object parts and use a fully-connected CRF as a post-processing step to obtain sharp boundaries.We also inject prior shape information in our model through a Restricted Boltzmann Machine, trained on ground-truth segmentations.Finally, we train a new fully-convolutional architecture from a random initialization, to segment different parts of the human brain in magnetic resonance image data.Our methods achieve state-of-the-art results on both types of data
Laifa, Oumeima. "A joint discriminative-generative approach for tumour angiogenesis assessment in computational pathology." Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS230.
Full textAngiogenesis is the process through which new blood vessels are formed from pre-existing ones. During angiogenesis, tumour cells secrete growth factors that activate the proliferation and migration of endothelial cells and stimulate over production of the vascular endothelial growth factor (VEGF). The fundamental role of vascular supply in tumour growth and anti-cancer therapies makes the evaluation of angiogenesis crucial in assessing the effect of anti-angiogenic therapies as a promising anti-cancer therapy. In this study, we establish a quantitative and qualitative panel to evaluate tumour blood vessels structures on non-invasive fluorescence images and histopathological slide across the full tumour to identify architectural features and quantitative measurements that are often associated with prediction of therapeutic response. We develop a Markov Random Field (MFRs) and Watershed framework to segment blood vessel structures and tumour micro-enviroment components to assess quantitatively the effect of the anti-angiogenic drug Pazopanib on the tumour vasculature and the tumour micro-enviroment interaction. The anti-angiogenesis agent Pazopanib was showing a direct effect on tumour network vasculature via the endothelial cells crossing the whole tumour. Our results show a specific relationship between apoptotic neovascularization and nucleus density in murine tumor treated by Pazopanib. Then, qualitative evaluation of tumour blood vessels structures is performed in whole slide images, known to be very heterogeneous. We develop a discriminative-generative neural network model based on both learning driven model convolutional neural network (CNN), and rule-based knowledge model Marked Point Process (MPP) to segment blood vessels in very heterogeneous images using very few annotated data comparing to the state of the art. We detail the intuition and the design behind the discriminative-generative model, and we analyze its similarity with Generative Adversarial Network (GAN). Finally, we evaluate the performance of the proposed model on histopathology slide and synthetic data. The limits of this promising framework as its perspectives are shown