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| EfficientNet× | Inception tīkls (GoogLeNet)× | |
|---|---|---|
| Nozare | Dziļā mācīšanās | Dziļā mācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2019 | 2015 |
| Autors≠ | Tan, M. & Le, Q. V. | Christian Szegedy et al. (Google) |
| Tips≠ | Compound-scaled convolutional neural network architecture | Deep CNN with parallel multi-scale convolutions |
| Pirmavots≠ | 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. link ↗ | Szegedy, C., et al. (2015). Going deeper with convolutions. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1–9. DOI ↗ |
| Citi nosaukumi | EfficientNet, compound scaling CNN, EfficientNet-B0 through B7, EfficientNetV2 | GoogLeNet, Inception v1, Deep Convolutional Neural Network (Google), Başlangıç Ağı |
| Saistītās≠ | 4 | 2 |
| Kopsavilkums≠ | 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. | The Inception Network, introduced by Szegedy et al. at Google in 2015 and submitted to CVPR under the name GoogLeNet, is a 22-layer deep convolutional neural network designed for large-scale image recognition. Its defining contribution is the Inception module, which applies convolutions of multiple kernel sizes in parallel and concatenates their outputs, enabling the network to capture spatial features at different scales simultaneously without a proportional increase in computational cost. |
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