2.4.4. Total Loss

In this study, the model is optimized through joint training of encoders, decoders, and discriminators in all spectral domains. The total loss function is the weighted sum of the counter loss, reconstruction loss, and latent matching loss in each spectral domain.

When a new spectral domain is added to the trained multi-spectral domain model, the model parameters of the existing spectral domain can be fixed, and the shared feature space can be exploited to accelerate the training process and avoid the expansion of training parameters.

$$\min\_{E,G} \max\_D L\_{\text{Total}}(E, G, D) = \lambda\_{\text{GAN}} L\_{\text{GAN}} + \lambda\_{\text{Recon}} L\_{\text{Recon}} + \lambda\_{\text{Match}} L\_{\text{Match}} \tag{9}$$

where *λ*GAN, *λ*Recon, and *λ*Match are weights that control the importance of loss terms.
