Academic literature on the topic 'Modèle génératif profond'
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Journal articles on the topic "Modèle génératif profond":
Kübler, Raoul V. "La révolution dévorera-t-elle ses enfants ? L’impact de l’IA générative et interactive sur le marketing opérationnel et stratégique." Décisions Marketing N° 112, no. 4 (January 25, 2024): 127–52. http://dx.doi.org/10.3917/dm.112.0127.
Shipway, Bradley, and Marilyn Joan Chaseling. "An Alberta Approach to School Improvement in an Australian Rural School." Alberta Journal of Educational Research 67, no. 3 (September 10, 2021): 297–311. http://dx.doi.org/10.55016/ojs/ajer.v67i3.69977.
BERTRAND, Denis. "La générativité est-elle soluble dans le sensible ? Réflexions topologiques et énonciatives « au cœur » du parcours génératif." 130, no. 130 (January 23, 2024). http://dx.doi.org/10.25965/as.8295.
Kilani, Mondher. "Identité." Anthropen, 2019. http://dx.doi.org/10.17184/eac.anthropen.122.
Gagné, Natacha. "Anthropologie et histoire." Anthropen, 2017. http://dx.doi.org/10.17184/eac.anthropen.060.
Dissertations / Theses on the topic "Modèle génératif profond":
Sadok, Samir. "Audiovisual speech representation learning applied to emotion recognition." Electronic Thesis or Diss., CentraleSupélec, 2024. http://www.theses.fr/2024CSUP0003.
Emotions are vital in our daily lives, becoming a primary focus of ongoing research. Automatic emotion recognition has gained considerable attention owing to its wide-ranging applications across sectors such as healthcare, education, entertainment, and marketing. This advancement in emotion recognition is pivotal for fostering the development of human-centric artificial intelligence. Supervised emotion recognition systems have significantly improved over traditional machine learning approaches. However, this progress encounters limitations due to the complexity and ambiguous nature of emotions. Acquiring extensive emotionally labeled datasets is costly, time-intensive, and often impractical.Moreover, the subjective nature of emotions results in biased datasets, impacting the learning models' applicability in real-world scenarios. Motivated by how humans learn and conceptualize complex representations from an early age with minimal supervision, this approach demonstrates the effectiveness of leveraging prior experience to adapt to new situations. Unsupervised or self-supervised learning models draw inspiration from this paradigm. Initially, they aim to establish a general representation learning from unlabeled data, akin to the foundational prior experience in human learning. These representations should adhere to criteria like invariance, interpretability, and effectiveness. Subsequently, these learned representations are applied to downstream tasks with limited labeled data, such as emotion recognition. This mirrors the assimilation of new situations in human learning. In this thesis, we aim to propose unsupervised and self-supervised representation learning methods designed explicitly for multimodal and sequential data and to explore their potential advantages in the context of emotion recognition tasks. The main contributions of this thesis encompass:1. Developing generative models via unsupervised or self-supervised learning for audiovisual speech representation learning, incorporating joint temporal and multimodal (audiovisual) modeling.2. Structuring the latent space to enable disentangled representations, enhancing interpretability by controlling human-interpretable latent factors.3. Validating the effectiveness of our approaches through both qualitative and quantitative analyses, in particular on emotion recognition task. Our methods facilitate signal analysis, transformation, and generation
Hadjeres, Gaëtan. "Modèles génératifs profonds pour la génération interactive de musique symbolique." Thesis, Sorbonne université, 2018. http://www.theses.fr/2018SORUS027/document.
This thesis discusses the use of deep generative models for symbolic music generation. We will be focused on devising interactive generative models which are able to create new creative processes through a fruitful dialogue between a human composer and a computer. Recent advances in artificial intelligence led to the development of powerful generative models able to generate musical content without the need of human intervention. I believe that this practice cannot be thriving in the future since the human experience and human appreciation are at the crux of the artistic production. However, the need of both flexible and expressive tools which could enhance content creators' creativity is patent; the development and the potential of such novel A.I.-augmented computer music tools are promising. In this manuscript, I propose novel architectures that are able to put artists back in the loop. The proposed models share the common characteristic that they are devised so that a user can control the generated musical contents in a creative way. In order to create a user-friendly interaction with these interactive deep generative models, user interfaces were developed. I believe that new compositional paradigms will emerge from the possibilities offered by these enhanced controls. This thesis ends on the presentation of genuine musical projects like concerts featuring these new creative tools
Hadjeres, Gaëtan. "Modèles génératifs profonds pour la génération interactive de musique symbolique." Electronic Thesis or Diss., Sorbonne université, 2018. http://www.theses.fr/2018SORUS027.
This thesis discusses the use of deep generative models for symbolic music generation. We will be focused on devising interactive generative models which are able to create new creative processes through a fruitful dialogue between a human composer and a computer. Recent advances in artificial intelligence led to the development of powerful generative models able to generate musical content without the need of human intervention. I believe that this practice cannot be thriving in the future since the human experience and human appreciation are at the crux of the artistic production. However, the need of both flexible and expressive tools which could enhance content creators' creativity is patent; the development and the potential of such novel A.I.-augmented computer music tools are promising. In this manuscript, I propose novel architectures that are able to put artists back in the loop. The proposed models share the common characteristic that they are devised so that a user can control the generated musical contents in a creative way. In order to create a user-friendly interaction with these interactive deep generative models, user interfaces were developed. I believe that new compositional paradigms will emerge from the possibilities offered by these enhanced controls. This thesis ends on the presentation of genuine musical projects like concerts featuring these new creative tools
Mehr, Éloi. "Unsupervised Learning of 3D Shape Spaces for 3D Modeling." Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS566.
Even though 3D data is becoming increasingly more popular, especially with the democratization of virtual and augmented experiences, it remains very difficult to manipulate a 3D shape, even for designers or experts. Given a database containing 3D instances of one or several categories of objects, we want to learn the manifold of plausible shapes in order to develop new intelligent 3D modeling and editing tools. However, this manifold is often much more complex compared to the 2D domain. Indeed, 3D surfaces can be represented using various embeddings, and may also exhibit different alignments and topologies. In this thesis we study the manifold of plausible shapes in the light of the aforementioned challenges, by deepening three different points of view. First of all, we consider the manifold as a quotient space, in order to learn the shapes’ intrinsic geometry from a dataset where the 3D models are not co-aligned. Then, we assume that the manifold is disconnected, which leads to a new deep learning model that is able to automatically cluster and learn the shapes according to their typology. Finally, we study the conversion of an unstructured 3D input to an exact geometry, represented as a structured tree of continuous solid primitives
Lucas, Thomas. "Modèles génératifs profonds : sur-généralisation et abandon de mode." Thesis, Université Grenoble Alpes, 2020. http://www.theses.fr/2020GRALM049.
This dissertation explores the topic of generative modelling of natural images,which is the task of fitting a data generating distribution.Such models can be used to generate artificial data resembling the true data, or to compress images.Latent variable models, which are at the core of our contributions, seek to capture the main factors of variations of an image into a variable that can be manipulated.In particular we build on two successful latent variable generative models, the generative adversarial network (GAN) and Variational autoencoder (VAE) models.Recently GANs significantly improved the quality of images generated by deep models, obtaining very compelling samples.Unfortunately these models struggle to capture all the modes of the original distribution, ie they do not cover the full variability of the dataset.Conversely, likelihood based models such as VAEs typically cover the full variety of the data well and provide an objective measure of coverage.However these models produce samples of inferior visual quality that are more easily distinguished from real ones.The work presented in this thesis strives for the best of both worlds: to obtain compelling samples while modelling the full support of the distribution.To achieve that, we focus on i) the optimisation problems used and ii) practical model limitations that hinder performance.The first contribution of this manuscript is a deep generative model that encodes global image structure into latent variables, built on the VAE, and autoregressively models low level detail.We propose a training procedure relying on an auxiliary loss function to control what information is captured by the latent variables and what information is left to an autoregressive decoder.Unlike previous approaches to such hybrid models, ours does not need to restrict the capacity of the autoregressive decoder to prevent degenerate models that ignore the latent variables.The second contribution builds on the standard GAN model, which trains a discriminator network to provide feedback to a generative network.The discriminator usually assesses the quality of individual samples, which makes it hard to evaluate the variability of the data.Instead we propose to feed the discriminator with emph{batches} that mix both true and fake samples, and train it to predict the ratio of true samples in the batch.These batches work as approximations of the distribution of generated images and allows the discriminator to approximate distributional statistics.We introduce an architecture that is well suited to solve this problem efficiently,and show experimentally that our approach reduces mode collapse in GANs on two synthetic datasets, and obtains good results on the CIFAR10 and CelebA datasets.The mutual shortcomings of VAEs and GANs can in principle be addressed by training hybrid models that use both types of objective.In our third contribution, we show that usual parametric assumptions made in VAEs induce a conflict between them, leading to lackluster performance of hybrid models.We propose a solution based on deep invertible transformations, that trains a feature space in which usual assumptions can be made without harm.Our approach provides likelihood computations in image space while being able to take advantage of adversarial training.It obtains GAN-like samples that are competitive with fully adversarial models while improving likelihood scores over existing hybrid models at the time of publication, which is a significant advancement
Prang, Mathieu. "Representation learning for symbolic music." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS489.
A key part in the recent success of deep language processing models lies in the ability to learn efficient word embeddings. These methods provide structured spaces of reduced dimensionality with interesting metric relationship properties. These, in turn, can be used as efficient input representations for handling more complex tasks. In this thesis, we focus on the task of learning embedding spaces for polyphonic music in the symbolic domain. To do so, we explore two different approaches.We introduce an embedding model based on a convolutional network with a novel type of self-modulated hierarchical attention, which is computed at each layer to obtain a hierarchical vision of musical information.Then, we propose another system based on VAEs, a type of auto-encoder that constrains the data distribution of the latent space to be close to a prior distribution. As polyphonic music information is very complex, the design of input representation is a crucial process. Hence, we introduce a novel representation of symbolic music data, which transforms a polyphonic score into a continuous signal.Finally, we show the potential of the resulting embedding spaces through the development of several creative applications used to enhance musical knowledge and expression, through tasks such as melodies modification or composer identification
Franceschi, Jean-Yves. "Apprentissage de représentations et modèles génératifs profonds dans les systèmes dynamiques." Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS014.
The recent rise of deep learning has been motivated by numerous scientific breakthroughs, particularly regarding representation learning and generative modeling. However, most of these achievements have been obtained on image or text data, whose evolution through time remains challenging for existing methods. Given their importance for autonomous systems to adapt in a constantly evolving environment, these challenges have been actively investigated in a growing body of work. In this thesis, we follow this line of work and study several aspects of temporality and dynamical systems in deep unsupervised representation learning and generative modeling. Firstly, we present a general-purpose deep unsupervised representation learning method for time series tackling scalability and adaptivity issues arising in practical applications. We then further study in a second part representation learning for sequences by focusing on structured and stochastic spatiotemporal data: videos and physical phenomena. We show in this context that performant temporal generative prediction models help to uncover meaningful and disentangled representations, and conversely. We highlight to this end the crucial role of differential equations in the modeling and embedding of these natural sequences within sequential generative models. Finally, we more broadly analyze in a third part a popular class of generative models, generative adversarial networks, under the scope of dynamical systems. We study the evolution of the involved neural networks with respect to their training time by describing it with a differential equation, allowing us to gain a novel understanding of this generative model
Grechka, Asya. "Image editing with deep neural networks." Electronic Thesis or Diss., Sorbonne université, 2023. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2023SORUS683.pdf.
Image editing has a rich history which dates back two centuries. That said, "classic" image editing requires strong artistic skills as well as considerable time, often in the scale of hours, to modify an image. In recent years, considerable progress has been made in generative modeling which has allowed realistic and high-quality image synthesis. However, real image editing is still a challenge which requires a balance between novel generation all while faithfully preserving parts of the original image. In this thesis, we will explore different approaches to edit images, leveraging three families of generative networks: GANs, VAEs and diffusion models. First, we study how to use a GAN to edit a real image. While methods exist to modify generated images, they do not generalize easily to real images. We analyze the reasons for this and propose a solution to better project a real image into the GAN's latent space so as to make it editable. Then, we use variational autoencoders with vector quantification to directly obtain a compact image representation (which we could not obtain with GANs) and optimize the latent vector so as to match a desired text input. We aim to constrain this problem, which on the face could be vulnerable to adversarial attacks. We propose a method to chose the hyperparameters while optimizing simultaneously the image quality and the fidelity to the original image. We present a robust evaluation protocol and show the interest of our method. Finally, we abord the problem of image editing from the view of inpainting. Our goal is to synthesize a part of an image while preserving the rest unmodified. For this, we leverage pre-trained diffusion models and build off on their classic inpainting method while replacing, at each denoising step, the part which we do not wish to modify with the noisy real image. However, this method leads to a disharmonization between the real and generated parts. We propose an approach based on calculating a gradient of a loss which evaluates the harmonization of the two parts. We guide the denoising process with this gradient
Cohen, Max. "Metamodel and bayesian approaches for dynamic systems." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAS003.
In this thesis, we develop deep learning architectures for modelling building energy consumption and air quality.We first present an end-to-end methodology for optimizing energy demand while improving indoor comfort, by substituting the traditionally used physical simulators with a much faster surrogate model.Using historic data, we can ensure that simulations from this metamodel match the real conditions of the buildings.Yet some differences remain, due to unavailable and random factors.We propose to quantify this uncertainty by combining state space models with time series deep learning models.In a first approach, we show how the weights of a model can be finetuned through Sequential Monte Carlo methods, in order to take into account uncertainty on the last layer.We propose a second generative model with discrete latent states, allowing for a simpler training procedure through Variational Inference and equivalent performances on a relative humidity forecasting task.Finally, our last work extends on these quantized models, by proposing a new prior based on diffusion bridges.By learning to corrupt and reconstruct samples from the latent space, our model is able to learn the complex prior distribution, regardless of the nature of the data
Cherti, Mehdi. "Deep generative neural networks for novelty generation : a foundational framework, metrics and experiments." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS029/document.
In recent years, significant advances made in deep neural networks enabled the creation of groundbreaking technologies such as self-driving cars and voice-enabled personal assistants. Almost all successes of deep neural networks are about prediction, whereas the initial breakthroughs came from generative models. Today, although we have very powerful deep generative modeling techniques, these techniques are essentially being used for prediction or for generating known objects (i.e., good quality images of known classes): any generated object that is a priori unknown is considered as a failure mode (Salimans et al., 2016) or as spurious (Bengio et al., 2013b). In other words, when prediction seems to be the only possible objective, novelty is seen as an error that researchers have been trying hard to eliminate. This thesis defends the point of view that, instead of trying to eliminate these novelties, we should study them and the generative potential of deep nets to create useful novelty, especially given the economic and societal importance of creating new objects in contemporary societies. The thesis sets out to study novelty generation in relationship with data-driven knowledge models produced by deep generative neural networks. Our first key contribution is the clarification of the importance of representations and their impact on the kind of novelties that can be generated: a key consequence is that a creative agent might need to rerepresent known objects to access various kinds of novelty. We then demonstrate that traditional objective functions of statistical learning theory, such as maximum likelihood, are not necessarily the best theoretical framework for studying novelty generation. We propose several other alternatives at the conceptual level. A second key result is the confirmation that current models, with traditional objective functions, can indeed generate unknown objects. This also shows that even though objectives like maximum likelihood are designed to eliminate novelty, practical implementations do generate novelty. Through a series of experiments, we study the behavior of these models and the novelty they generate. In particular, we propose a new task setup and metrics for selecting good generative models. Finally, the thesis concludes with a series of experiments clarifying the characteristics of models that can exhibit novelty. Experiments show that sparsity, noise level, and restricting the capacity of the net eliminates novelty and that models that are better at recognizing novelty are also good at generating novelty
Book chapters on the topic "Modèle génératif profond":
GRASLIN, A., R. DUPONT, O. CHABERT, Ph ROUANET DE BERCHOUX, and P. DERAIN. "Du concept à la réalisation." In Médecine et Armées Vol. 45 No.2, 103–12. Editions des archives contemporaines, 2017. http://dx.doi.org/10.17184/eac.7421.