SlowGuess/NeurIPS_2023_OCR huggingface dataset:
In line with the experiments presented in § 5, we compare the nonparametric model (blue) to misspecified baselines: one with a correctly specified nonparametric latent model but employing a linear encoder (red), and a model with a nonlinear encoder but assuming a causal graph with no arrows (orange). The results shown in Fig. 6 show that the nonparametric model accurately identifies the latent variables, benefiting from both the nonlinear encoder and the explicit modelling of causal dependence. When we compare the nonparametric model trained on the location-scale data generating process (left blue violin) to one trained on the linear Gaussian process (right blue violin), we observe that in the location-scale case it seems to be more challenging for the model to uncover the true latent factors.