Lacharme Guillaume, Cardot Hubert, Lente Christophe, Monmarche Nicolas
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引用次数: 0
Abstract
In this paper, we will provide a detailed explanation of the limitations behind differentiable architecture search (DARTS). Algorithms based on the DARTS paradigm tend to converge towards degenerate solutions. A degenerate solution corresponds to an architecture with a shallow graph containing mainly skip connections. We have identified 6 sources of errors that could explain this phenomenon. Some of these errors can only be partially eliminated. Therefore, we will propose an innovative solution to remove degenerate solutions from the search space. We will demonstrate the validity of our approach through experiments conducted on the CIFAR10 and CIFAR100 databases. Our code is available at the following link: https://scm.univ-tours.fr/projetspublics/lifat/darts_ibpria_sparcity
期刊介绍：
The journal publishes high quality articles in areas of fundamental research in intelligent pattern analysis and applications in computer science and engineering. It aims to provide a forum for original research which describes novel pattern analysis techniques and industrial applications of the current technology. In addition, the journal will also publish articles on pattern analysis applications in medical imaging. The journal solicits articles that detail new technology and methods for pattern recognition and analysis in applied domains including, but not limited to, computer vision and image processing, speech analysis, robotics, multimedia, document analysis, character recognition, knowledge engineering for pattern recognition, fractal analysis, and intelligent control. The journal publishes articles on the use of advanced pattern recognition and analysis methods including statistical techniques, neural networks, genetic algorithms, fuzzy pattern recognition, machine learning, and hardware implementations which are either relevant to the development of pattern analysis as a research area or detail novel pattern analysis applications. Papers proposing new classifier systems or their development, pattern analysis systems for real-time applications, fuzzy and temporal pattern recognition and uncertainty management in applied pattern recognition are particularly solicited.
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