方法证据记录
CNN Image Classification
CNN image classification uses deep convolutional architectures such as ResNet (He et al., 2016), VGG and EfficientNet (Tan & Le, 2019) to sort images into categories. Stacked convolutional layers learn a hierarchy of visual features directly from pixels, and skip (residual) connections prevent the vanishing-gradient problem in very deep networks.
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。
Convolutional Neural Network Image Classification (ResNet / VGG / EfficientNet)
分类方法记录 · ml-model / deep-learning
- He, K., Zhang, X., Ren, S. & Sun, J. (2016). Deep Residual Learning for Image Recognition. CVPR. · DOI 10.1109/CVPR.2016.90
- Tan, M. & Le, Q.V. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. ICML, PMLR 97, 6105–6114. arXiv:1905.11946. · URL
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