To develop an adaptive or dynamic model configuration that automatically determines the optimal network depth for a given HSI dataset, a few strategies can be considered:
Hyperparameter Optimization: Implement automated hyperparameter optimization techniques, such as Bayesian optimization or genetic algorithms, to search for the optimal encoder depth based on performance metrics like accuracy or loss. This approach can iteratively adjust the depth of Transformer encoders during training to find the most suitable configuration.
Dynamic Network Pruning: Utilize dynamic network pruning methods that adjust the network depth during training based on the importance of different encoder layers. By monitoring the impact of each layer on the overall performance, the model can dynamically prune or expand the network depth to optimize classification accuracy.
Adaptive Learning Rate Scheduling: Implement adaptive learning rate scheduling techniques that adjust the learning rate based on the network's performance at different depths. By dynamically modifying the learning rate for each encoder layer, the model can adapt its training process to find the optimal depth configuration.
By integrating these adaptive strategies into the training process, the 3D-ConvSST architecture can automatically determine the optimal network depth for different HSI datasets, improving classification performance and efficiency.