Add to ORKGDeep neural network ensembles hold the potential of improving generalization performance for complex learning tasks. This paper presents formal analysis and empirical evaluation to show that heterogeneous deep ensembles with high ensemble diversity can effectively leverage model learning heterogeneity to boost ensemble robustness. We first show that heterogeneous DNN models trained for solving the same learning problem, e.g., object detection, can significantly strengthen the mean average precision (mAP) through our weighted bounding box ensemble consensus method. Second, we further compose ensembles of heterogeneous models for solving different learning problems, e.g., object detection and semantic segmentation, by introducing the connecte...
Ensembling neural networks is an effective way to increase accuracy, and can often match the perform...
Ensemble learning of neural network is a learning paradigm where ensembles of several neural network...
Due to numerous breakthroughs in real-world applications brought by machine intelligence, deep neura...
The performance decay experienced by deep neural networks (DNNs) when confronted with distributional...
Deep neural networks (DNNs) have become the technology of choice for realizing a variety of complex ...
Training an ensemble of different sub-models has empirically proven to be an effective strategy to i...
Ensembling a neural network is a widely recognized approach to enhance model performance, estimate u...
Learning-based classifiers are susceptible to adversarial examples. Existing defence methods are mos...
Ensemble multifeatured deep learning methodology has emerged as a powerful approach to overcome the ...
Recent years have witnessed the remarkable success of deep neural network (DNN) models spanning a wi...
Though deep learning has been applied successfully in many scenarios, malicious inputs with human-im...
Often the best performing deep neural models are ensembles of multiple base-level networks. Unfortun...
Learning in uncertain, noisy, or adversarial environments is a challenging task for deep neural netw...
Although deep learning models have achieved state-of-the art performance on a number of vision tasks...
Though deep learning has been applied successfully in many scenarios, malicious inputs with human-im...
Ensembling neural networks is an effective way to increase accuracy, and can often match the perform...
Ensemble learning of neural network is a learning paradigm where ensembles of several neural network...
Due to numerous breakthroughs in real-world applications brought by machine intelligence, deep neura...
The performance decay experienced by deep neural networks (DNNs) when confronted with distributional...
Deep neural networks (DNNs) have become the technology of choice for realizing a variety of complex ...
Training an ensemble of different sub-models has empirically proven to be an effective strategy to i...
Ensembling a neural network is a widely recognized approach to enhance model performance, estimate u...
Learning-based classifiers are susceptible to adversarial examples. Existing defence methods are mos...
Ensemble multifeatured deep learning methodology has emerged as a powerful approach to overcome the ...
Recent years have witnessed the remarkable success of deep neural network (DNN) models spanning a wi...
Though deep learning has been applied successfully in many scenarios, malicious inputs with human-im...
Often the best performing deep neural models are ensembles of multiple base-level networks. Unfortun...
Learning in uncertain, noisy, or adversarial environments is a challenging task for deep neural netw...
Although deep learning models have achieved state-of-the art performance on a number of vision tasks...
Though deep learning has been applied successfully in many scenarios, malicious inputs with human-im...
Ensembling neural networks is an effective way to increase accuracy, and can often match the perform...
Ensemble learning of neural network is a learning paradigm where ensembles of several neural network...
Due to numerous breakthroughs in real-world applications brought by machine intelligence, deep neura...