What are the model fusion techniques in Torch?

In Torch, several model integration techniques can be used, including:

1. Model ensembling involves combining multiple independently trained models by aggregating their predictions through methods such as voting, averaging, or weighting in order to improve overall performance and generalization.
2. Knowledge Distillation refers to transferring the knowledge of a complex model (teacher model) to a simplified model (student model) by training the student model using the teacher model’s output as soft labels. This allows for reducing the complexity and computational cost of the model while maintaining performance.
3. Layer Fusion: combining specific layers or modules from multiple models, such as stacking or connecting convolutional layers or fully connected layers from different models, to construct a more powerful and complex deep neural network.
4. Feature fusion: combining features extracted from different models by simple concatenation, weighted multiplication, element addition, etc. to create a more diverse and enriched feature representation.
5. Data Augmentation Fusion: Enhancing the same dataset with various data augmentation strategies during training, then inputting the augmented data into different models for training. Finally, merging the predictions of multiple models to improve the model’s robustness and generalization ability.