方法证据记录
EfficientNet
EfficientNet is a family of convolutional neural network architectures introduced by Mingxing Tan and Quoc V. Le (Google Brain) at ICML 2019 that systematically co-scales network depth, width, and input resolution using a single compound coefficient, achieving state-of-the-art image classification accuracy with substantially fewer parameters and FLOPs than prior networks such as ResNet and Inception.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
分类方法记录 · ml-model / deep-learning
- Tan, M. & Le, Q. V. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of the 36th International Conference on Machine Learning (ICML 2019), PMLR 97, 6105–6114. · URL
- Goodfellow, I., Bengio, Y. & Courville, A. (2016). Deep Learning. MIT Press. · ISBN 978-0-262-03561-3
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