from pydantic.dataclasses import dataclass
from ..base.base_config import BaseMultiVAEConfig
[docs]
@dataclass
class MVTCAEConfig(BaseMultiVAEConfig):
"""This is the base config class for the MVTCAE model from
'Multi-View Representation Learning via Total Correlation Objective' Neurips 2021.
The code is based on the original implementation that can be found here :
https://github.com/gr8joo/MVTCAE/blob/master/run_epochs.py.
Args:
n_modalities (int): The number of modalities. Default: None.
latent_dim (int): The dimension of the latent space. Default: None.
input_dims (dict[str,tuple]) : The modalities'names (str) and input shapes (tuple).
uses_likelihood_rescaling (bool): To mitigate modality collapse, it is possible to use likelihood rescaling.
(see : https://proceedings.mlr.press/v162/javaloy22a.html).
The inputs_dim must be provided to compute the likelihoods rescalings. It is used in a number of models
which is why we include it here. Default to False.
rescale_factors (dict[str, float]): The reconstruction rescaling factors per modality.
If None is provided but uses_likelihood_rescaling is True, a default value proportional to the input modality
size is computed. Default to None.
decoders_dist (Dict[str, Union[function, str]]). The decoder distributions to use per modality.
Per modality, you can provide a string in ['normal','bernoulli','laplace']. For Bernoulli distribution,
the decoder is expected to output **logits**. If None is provided, a normal distribution is used for each modality.
decoder_dist_params (Dict[str,dict]) : Parameters for the output decoder distributions, for
computing the log-probability.
For instance, with normal or laplace distribution, you can pass the scale in this dictionary with
:code:`decoder_dist_params = {'mod1' : {"scale" : 0.75}}`.
alpha (float): The parameter that ponderates the total correlation ratio in the loss. Default to 0.1
beta (float): The parameter that weights the sum of all KLs. Default to 2.5.
"""
alpha: float = 0.1
beta: float = 2.5