latte.functional.bundles.dependency_aware_mutual_info

Module Contents

Functions

dependency_aware_mutual_info_bundle(z: numpy.ndarray, a: numpy.ndarray, reg_dim: Optional[List] = None, discrete: bool = False) → Dict[str, numpy.ndarray]

Calculate between latent vectors and attributes:
latte.functional.bundles.dependency_aware_mutual_info.dependency_aware_mutual_info_bundle(z: numpy.ndarray, a: numpy.ndarray, reg_dim: Optional[List] = None, discrete: bool = False) → Dict[str, numpy.ndarray]
Calculate between latent vectors and attributes:

  • Mutual Information Gap (MIG)

  • Dependency-Aware Mutual Information Gap (DMIG)

  • Dependency-Blind Mutual Information Gap (XMIG)

  • Dependency-Aware Latent Information Gap (DLIG)

Parameters

  • z (np.ndarray, (n_samples, n_features)) – a batch of latent vectors

  • a (np.ndarray, (n_samples, n_attributes) or (n_samples,)) – a batch of attribute(s)

  • reg_dim (Optional[List], optional) – regularized dimensions, by default None Attribute a[:, i] is regularized by z[:, reg_dim[i]]. If None, a[:, i] is assumed to be regularized by z[:, i].

  • discrete (bool, optional) – Whether the attributes are discrete, by default False

Returns

A dictionary of mutual information metrics with keys [‘MIG’, ‘DMIG’, ‘XMIG’, ‘DLIG’] each mapping to a corresponding metric np.ndarray of shape (n_attributes,).

Return type

Dict[str, np.ndarray]

References

1

  1. Chen, X. Li, R. Grosse, and D. Duvenaud, “Isolating sources of disentanglement in variational autoencoders”, in Proceedings of the 32nd International Conference on Neural Information Processing Systems, 2018.

2

    1. Watcharasupat and A. Lerch, “Evaluation of Latent Space Disentanglement in the Presence of Interdependent Attributes”, in Extended Abstracts of the Late-Breaking Demo Session of the 22nd International Society for Music Information Retrieval Conference, 2021.

3

    1. Watcharasupat, “Controllable Music: Supervised Learning of Disentangled Representations for Music Generation”, 2021.