efficientnet: rethinking model scaling for convolutional neural networks

EfficientNet是一种新型的卷积神经网络模型，它重新思考了模型缩放的问题。它通过使用复合缩放方法，即同时缩放深度、宽度和分辨率，来提高模型的效率和准确性。这种方法可以在不增加计算成本的情况下，显著提高模型的性能。EfficientNet已经在多个计算机视觉任务上取得了优异的表现，包括图像分类、目标检测和语义分割等。

相关问题

MobileNetV3: Searching for MobileNetV3 参考文献

1. Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam. "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications." arXiv preprint arXiv:1704.04861 (2017).

2. Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen. "Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation." arXiv preprint arXiv:1801.04381 (2018).

3. Andrew G. Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, Quoc V. Le, Hartwig Adam. "Searching for MobileNetV3." arXiv preprint arXiv:1905.02244 (2019).

4. Mingxing Tan, Quoc V. Le. "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks." arXiv preprint arXiv:1905.11946 (2019).

5. Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich. "Going Deeper with Convolutions." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.

resnet网络模型和efficient模型第一次出现的时间

ResNet网络模型最早出现在2015年的论文《Deep Residual Learning for Image Recognition》中，由Kaiming He等人提出。而Efficient模型则是在2019年的论文《EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks》中，由Mingxing Tan和Quoc V. Le提出。