In amortised neural inference, the amortisation gap refers to the error introduced due to incomplete training or limitations in the flexibility of the neural network. To reduce this gap and improve the performance of amortised neural inference methods, several strategies can be employed:
Data Augmentation: Increasing the diversity and quantity of training data can help the neural network learn a more robust mapping between the data and the inferential targets. Augmenting the dataset with variations of existing data points can help the network generalize better.
Regularization Techniques: Applying regularization methods such as L1 or L2 regularization, dropout, or batch normalization can prevent overfitting and improve the generalization capabilities of the neural network.
Architecture Design: Choosing an appropriate neural network architecture that is well-suited for the specific task at hand can help reduce the amortisation gap. Complex architectures like deep neural networks or convolutional neural networks may capture more intricate relationships in the data.
Hyperparameter Tuning: Optimizing hyperparameters such as learning rate, batch size, and network depth can significantly impact the performance of the neural network and reduce the amortisation gap.
Ensemble Methods: Utilizing ensemble methods by combining predictions from multiple neural networks can help mitigate errors introduced by individual networks and improve overall performance.
Transfer Learning: Leveraging pre-trained neural networks or using transfer learning techniques can accelerate training and improve the performance of the network, reducing the amortisation gap.
By implementing these strategies, the amortisation gap in amortised neural inference methods can be minimized, leading to more accurate and efficient inference results.