- As there only two parameters per feature map, the total number of BN parameters comprise less than 1% of the total number of parameters of a pre-trained ResNet.
- To summarize, our contributions are three fold:
- Specifically, we applied CBN to a pre-trained ResNet, leading to the proposed MODERN architecture.
- trainable scalars introduced to keep the representational power of the original network.
- As we will explain in the next section, adhering to this assumption will give rise to a structure of the discriminator that requires us to take an inner product between the embedded condition vector y and the feature vector
- \ie可以被替换为“including”
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转载自blog.csdn.net/chengsilin666/article/details/103962358
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