MONCAE: Multi-Objective Neuroevolution of Convolutional Autoencoders
In this paper, we present a novel neuroevolutionary method to identify the architecture and hyperparameters of convolutional autoencoders. Remarkably, we used a hypervolume indicator in the context of neural architecture search for autoencoders, for the first time to our current knowledge. Results show that images were compressed by a factor of more than 10, while still retaining enough information to achieve image classification for the majority of the tasks. Thus, this new approach can be used to speed up the AutoML pipeline for image compression.
Code (0)
등록된 구현이 없습니다.
Tasks
AutoMLimage-classificationImage ClassificationImage CompressionNeural Architecture SearchSimilar Papers 제목 키워드 기반
Neuroevolutionary Multi-objective approaches to Trajectory Prediction in Autonomous Vehicles
The incentive for using Evolutionary Algorithms (EAs) for the automated optimization and training of deep neural networks (DNNs), a process referred to as neuroevolution, has gained momentum in recent years. The configur…
Autonomous VehiclesEvolutionary AlgorithmsTrajectory PredictionNeuroLGP-SM: Scalable Surrogate-Assisted Neuroevolution for Deep Neural Networks
Evolutionary Algorithms (EAs) play a crucial role in the architectural configuration and training of Artificial Deep Neural Networks (DNNs), a process known as neuroevolution. However, neuroevolution is hindered by its i…
Evolutionary AlgorithmsEvolutionary Multi-objective Optimisation in Neurotrajectory Prediction
Machine learning has rapidly evolved during the last decade, achieving expert human performance on notoriously challenging problems such as image classification. This success is partly due to the re-emergence of bio-insp…
Evolutionary Algorithmsimage-classificationImage ClassificationPrediction+2AD-NEv++ : The multi-architecture neuroevolution-based multivariate anomaly detection framework
Anomaly detection tools and methods enable key analytical capabilities in modern cyberphysical and sensor-based systems. Despite the fast-paced development in deep learning architectures for anomaly detection, model opti…
Anomaly DetectionModel OptimizationMany-Objective Neuroevolution for Testing Games
Generating tests for games is challenging due to the high degree of randomisation inherent to games and hard-to-reach program states that require sophisticated gameplay. The test generator NEATEST tackles these challenge…
software testing