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 ## Instructions for Integrating Disentanglement Learning into MOSA
 To incorporate disentanglement learning, two additional terms are included in the loss function, following the Disentangled Inferred Prior Variational Autoencoder (DIP-VAE) approach, as described by [Kumar et al. (2018)](https://arxiv.org/abs/1711.00848):
 
-![DIP-VAE loss term](./figure/dipvae_lossterm.png)
 
 To use this, update the `hyperparameters.json` file by specifying `dip_vae_type` as either `"i"` or `"ii"` (type ii is recommended), and define the parameters `lambda_d` and `lambda_od` as float values, which control the diagonal and off-diagonal regularization, respectively.