To enhance the generalization and transferability capabilities of TempNet, several improvements can be considered:
Incorporating Attention Mechanisms: Introducing attention mechanisms in TempNet can help the network focus on relevant parts of the input data, improving its ability to capture important features for temperature prediction.
Regularization Techniques: Implementing regularization techniques such as dropout or weight decay can prevent overfitting and improve the network's generalization to unseen data.
Data Augmentation: Augmenting the training data with various transformations can help TempNet learn robust features and improve its performance on diverse datasets.
Ensemble Learning: Training multiple TempNet models with different architectures or initializations and combining their predictions can enhance generalization and robustness.
Adaptive Learning Rates: Implementing adaptive learning rate strategies can help TempNet converge faster and improve its ability to generalize to different datasets.