Essays about: "Flow-based Models"
Showing result 1 - 5 of 12 essays containing the words Flow-based Models.
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1. Exploring Normalizing Flow Modifications for Improved Model Expressivity
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Normalizing flows represent a class of generative models that exhibit a number of attractive properties, but do not always achieve state-of-the-art performance when it comes to perceived naturalness of generated samples. To improve the quality of generated samples, this thesis examines methods to enhance the expressivity of discrete-time normalizing flow models and thus their ability to capture different aspects of the data. READ MORE
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2. Image generation through feature extraction and learning using a deep learning approach
University essay from Linnéuniversitetet/Institutionen för datavetenskap och medieteknik (DM)Abstract : With recent advancements, image generation has become more and more possible with the introduction of stronger generative artificial intelligence (AI) models. The idea and ability of generating non-existing images that highly resemble real world images is interesting for many use cases. READ MORE
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3. Deep Generative Modeling : An Overview of Recent Advances in Likelihood-based Models and an Application to 3D Point Cloud Generation
University essay from Umeå universitet/Institutionen för matematik och matematisk statistikAbstract : Deep generative modeling refers to the process of constructing a model, parameterized by a deep neural network, that learns the underlying patterns and structures of the data generating process which produced the samples in a given dataset, in order to generate novel samples that resemble those in the original dataset. Deep generative models for 3D shape generation hold significant importance to various fields including robotics, medical imaging, manufacturing, computer animation and more. READ MORE
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4. Conditional Generative Flow for Street Scene Generation
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Generative modeling is a major branch of machine learning attributed to designing models that can learn how data are generated and hence are able to synthesize novel data. With the recent advancements in deep learning, generative models have been improved significantly and successfully applied in a variety of domains, including computer vision, video generation, audio generation, and even in medical applications. READ MORE
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5. Normalizing Flow based Hidden Markov Models for Phone Recognition
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : The task of Phone recognition is a fundamental task in Speech recognition and often serves a critical role in bench-marking purposes. Researchers have used a variety of models used in the past to address this task, using both generative and discriminative learning approaches. READ MORE
