Y Cao, TA Geddes, JYH Yang, P Yang - Nature Machine Intelligence, 2020 - nature.com
The remarkable flexibility and adaptability of ensemble methods and deep learning models have led to the proliferation of their application in bioinformatics research. Traditionally …
In recent years, deep neural networks have been successful in both industry and academia, especially for computer vision tasks. The great success of deep learning is mainly due to its …
MT Ahad, Y Li, B Song, T Bhuiyan - Artificial Intelligence in Agriculture, 2023 - Elsevier
Although convolutional neural network (CNN) paradigms have expanded to transfer learning and ensemble models from original individual CNN architectures, few studies have …
Z Allen-Zhu, Y Li - arXiv preprint arXiv:2012.09816, 2020 - arxiv.org
We formally study how ensemble of deep learning models can improve test accuracy, and how the superior performance of ensemble can be distilled into a single model using …
C He, M Annavaram… - Advances in Neural …, 2020 - proceedings.neurips.cc
Scaling up the convolutional neural network (CNN) size (eg, width, depth, etc.) is known to effectively improve model accuracy. However, the large model size impedes training on …
Often we wish to transfer representational knowledge from one neural network to another. Examples include distilling a large network into a smaller one, transferring knowledge from …
J She, Y Hu, H Shi, J Wang… - Proceedings of the …, 2021 - openaccess.thecvf.com
Due to the subjective annotation and the inherent inter-class similarity of facial expressions, one of key challenges in Facial Expression Recognition (FER) is the annotation ambiguity …
F Tung, G Mori - Proceedings of the IEEE/CVF international …, 2019 - openaccess.thecvf.com
Abstract Knowledge distillation is a widely applicable technique for training a student neural network under the guidance of a trained teacher network. For example, in neural network …
JH Cho, B Hariharan - Proceedings of the IEEE/CVF …, 2019 - openaccess.thecvf.com
In this paper, we present a thorough evaluation of the efficacy of knowledge distillation and its dependence on student and teacher architectures. Starting with the observation that more …