Unsupervised image segmentation by mutual information maximization and adversarial regularization
SE Mirsadeghi, A Royat… - IEEE Robotics and …, 2021 - ieeexplore.ieee.org
Semantic segmentation is one of the basic, yet essential scene understanding tasks for an
autonomous agent. The recent developments in supervised machine learning and neural
networks have enjoyed great success in enhancing the performance of the state-of-the-art
techniques for this task. However, their superior performance is highly reliant on the
availability of a large-scale annotated dataset. In this letter, we propose a novel fully
unsupervised semantic segmentation method, the so-called Information Maximization and …
autonomous agent. The recent developments in supervised machine learning and neural
networks have enjoyed great success in enhancing the performance of the state-of-the-art
techniques for this task. However, their superior performance is highly reliant on the
availability of a large-scale annotated dataset. In this letter, we propose a novel fully
unsupervised semantic segmentation method, the so-called Information Maximization and …
Unsupervised Image Segmentation by Mutual Information Maximization and Adversarial Regularization
S Ehsan Mirsadeghi, A Royat, H Rezatofighi - arXiv e-prints, 2021 - ui.adsabs.harvard.edu
Semantic segmentation is one of the basic, yet essential scene understanding tasks for an
autonomous agent. The recent developments in supervised machine learning and neural
networks have enjoyed great success in enhancing the performance of the state-of-the-art
techniques for this task. However, their superior performance is highly reliant on the
availability of a large-scale annotated dataset. In this paper, we propose a novel fully
unsupervised semantic segmentation method, the so-called Information Maximization and …
autonomous agent. The recent developments in supervised machine learning and neural
networks have enjoyed great success in enhancing the performance of the state-of-the-art
techniques for this task. However, their superior performance is highly reliant on the
availability of a large-scale annotated dataset. In this paper, we propose a novel fully
unsupervised semantic segmentation method, the so-called Information Maximization and …
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