resnet最后的全连接层能不能换为MLP?

有没有大佬解答一下，这样分类的效果会更好吗

这有什么区别吗

可以，这俩是同父同母的兄弟[em1][em1]

两层的mlp就是全连接层

Using a Multi-Layer Perceptron (MLP) to replace the final fully connected (FC) layer of a Residual Network (ResNet) is a concept that has been explored in various machine learning research works. The idea is to leverage the capacity of MLPs to capture complex relationships in the data, potentially improving the model's performance. From the provided search results, we can see that there are studies and experiments that have been conducted in this direction. For instance, the discussion in the PyTorch Forums (references 9 and 10) suggests that it is possible to pass the features from a ResNet model to an MLP. This implies that replacing the FC layer with an MLP is a feasible approach. Moreover, the concept of using MLPs in place of convolutional layers or in conjunction with them has been gaining traction, as seen with the introduction of models like RepMLP (reference 1) and ResMLP (reference 5). These models have demonstrated that MLPs can be effectively integrated into neural network architectures for image classification tasks, sometimes leading to improved performance. However, whether replacing the final FC layer with an MLP will improve the performance of a ResNet model depends on several factors, including: 1. **The complexity of the task**: If the classification task benefits from the global interactions between features that MLPs can capture, it might lead to improved performance. 2. **The size and architecture of the MLP**: The number of layers and the size of each layer in the MLP can significantly impact the model's capacity to learn from the data. 3. **Training procedures**: The way the MLP is trained, including the optimization algorithms, learning rates, and regularization techniques, can affect the final performance. 4. **Data augmentations and preprocessing**: As mentioned in reference 35, the use of strong data augmentations can influence the training dynamics and the need for inductive biases provided by convolutional layers. It's important to note that while MLPs offer flexibility and have shown promise in certain scenarios, they might also introduce additional parameters and computational complexity. Therefore, a thorough evaluation and potentially a hyperparameter search would be necessary to determine if such a replacement leads to a net improvement for a specific application. In conclusion, while there is evidence that MLPs can be used to replace the final FC layer of ResNet and potentially enhance performance, the actual improvement would depend on the particularities of the given task, dataset, and the specific configuration of the MLP. It is an empirical question that would require validation through experimentation.

我要学混了，mlp不是gnn里面的吗？还是说别的神经网络也能用？

都能用，都是改模型结构而已

他的性能大概率不是改点这么简单的层级能决定的。