Beyond masked autoencoding, other self-supervised pretraining objectives that could be explored to enhance the model's ability to learn comprehensive representations from multimodal biosignals include contrastive learning, temporal context prediction, and generative modeling.
Contrastive Learning: By training the model to learn representations that maximize agreement between augmented views of the same input and minimize agreement between views of different inputs, contrastive learning can help the model capture meaningful relationships and patterns within the multimodal biosignals.
Temporal Context Prediction: By predicting the future or past states of the biosignals based on the current context, the model can learn to understand the temporal dependencies and patterns present in the data, leading to more robust and informative representations.
Generative Modeling: By training the model to generate realistic samples of the biosignals, generative modeling can help the model capture the underlying distribution of the data and learn to represent the complex relationships and structures within the multimodal biosignals.
Exploring these alternative self-supervised pretraining objectives in conjunction with masked autoencoding can provide the model with diverse learning signals and perspectives, enabling it to learn more comprehensive and informative representations from multimodal biosignals. By combining multiple self-supervised learning objectives, researchers can enhance the model's ability to capture the rich and complex information present in multimodal biosignals, leading to improved performance and generalization across various tasks and modalities.