Fig 11. PS-VAE hyperparameter selection for the head-fixed mouse dataset.

A: MSE per pixel as a function of latent dimensionality and the hyperparameter α, which controls the strength of the label reconstruction term. The frame reconstruction is robust across many orders of magnitude. B: MSE per label as a function of latent dimensionality and α. As the latent dimensionality increases the model becomes more robust to α, but is sensitive to this value when the model has few latents due to the strong tradeoff between frame and label reconstruction. Subsequent panels detail β and γ with a 6D model and α = 1000. C: MSE per pixel as a function of β and γ; frame reconstruction is robust to both of these hyperprameters. D: MSE per label as a function of β and γ; label reconstruction is robust to both of these hyperprameters. E: Index code mutual information (ICMI; see Eq 13) as a function of β and γ. The ICMI, although not explicitly penalized by β, is affected by this hyperparameter. F: Total Correlation (TC) as a function of β and γ. Increasing β decreases the TC as desired. G: Dimension-wise KL (DWKL) as a function of β and γ. The DWKL, although not explicitly penalized by β, is affected by this hyperparameter. H: Pearson correlation in the model’s 2D unsupervised subspace as a function of β and γ. I: The subspace overlap as defined by ‖UU^T − I‖² (where U = [A;B] and I the identity) as a function of β and γ. Increasing γ leads to an orthogonalized latent space, while varying β has no effect. J: Example subspace overlap matrix (UU^T) for γ = 0. The upper left 4x4 block represents the supervised subspace, the lower right 2x2 block represents the unusupervised subspace. K: Example subspace overlap matrix for γ = 1000; the subspace is close to orthogonal. Error bars in panels A-D represent 95% bootstrapped confidence interval over test trials; line plots in panels E-H are the mean values over test trials, and confidence intervals are omitted for clarity.