328 research outputs found
Evaluating Disentanglement in Generative Models Without Knowledge of Latent Factors
Probabilistic generative models provide a flexible and systematic framework
for learning the underlying geometry of data. However, model selection in this
setting is challenging, particularly when selecting for ill-defined qualities
such as disentanglement or interpretability. In this work, we address this gap
by introducing a method for ranking generative models based on the training
dynamics exhibited during learning. Inspired by recent theoretical
characterizations of disentanglement, our method does not require supervision
of the underlying latent factors. We evaluate our approach by demonstrating the
need for disentanglement metrics which do not require labels\textemdash the
underlying generative factors. We additionally demonstrate that our approach
correlates with baseline supervised methods for evaluating disentanglement.
Finally, we show that our method can be used as an unsupervised indicator for
downstream performance on reinforcement learning and fairness-classification
problems
Modelling Cellular Perturbations with the Sparse Additive Mechanism Shift Variational Autoencoder
Generative models of observations under interventions have been a vibrant
topic of interest across machine learning and the sciences in recent years. For
example, in drug discovery, there is a need to model the effects of diverse
interventions on cells in order to characterize unknown biological mechanisms
of action. We propose the Sparse Additive Mechanism Shift Variational
Autoencoder, SAMS-VAE, to combine compositionality, disentanglement, and
interpretability for perturbation models. SAMS-VAE models the latent state of a
perturbed sample as the sum of a local latent variable capturing
sample-specific variation and sparse global variables of latent intervention
effects. Crucially, SAMS-VAE sparsifies these global latent variables for
individual perturbations to identify disentangled, perturbation-specific latent
subspaces that are flexibly composable. We evaluate SAMS-VAE both
quantitatively and qualitatively on a range of tasks using two popular single
cell sequencing datasets. In order to measure perturbation-specific
model-properties, we also introduce a framework for evaluation of perturbation
models based on average treatment effects with links to posterior predictive
checks. SAMS-VAE outperforms comparable models in terms of generalization
across in-distribution and out-of-distribution tasks, including a combinatorial
reasoning task under resource paucity, and yields interpretable latent
structures which correlate strongly to known biological mechanisms. Our results
suggest SAMS-VAE is an interesting addition to the modeling toolkit for machine
learning-driven scientific discovery.Comment: Presented at the 37th Conference on Neural Information Processing
Systems (NeurIPS 2023) (Post-NeurIPS fixes: cosmetic fixes, updated
references, added simulation to appendix
Gated Variational AutoEncoders: Incorporating Weak Supervision to Encourage Disentanglement
Variational AutoEncoders (VAEs) provide a means to generate representational
latent embeddings. Previous research has highlighted the benefits of achieving
representations that are disentangled, particularly for downstream tasks.
However, there is some debate about how to encourage disentanglement with VAEs
and evidence indicates that existing implementations of VAEs do not achieve
disentanglement consistently. The evaluation of how well a VAE's latent space
has been disentangled is often evaluated against our subjective expectations of
which attributes should be disentangled for a given problem. Therefore, by
definition, we already have domain knowledge of what should be achieved and yet
we use unsupervised approaches to achieve it. We propose a weakly-supervised
approach that incorporates any available domain knowledge into the training
process to form a Gated-VAE. The process involves partitioning the
representational embedding and gating backpropagation. All partitions are
utilised on the forward pass but gradients are backpropagated through different
partitions according to selected image/target pairings. The approach can be
used to modify existing VAE models such as beta-VAE, InfoVAE and DIP-VAE-II.
Experiments demonstrate that using gated backpropagation, latent factors are
represented in their intended partition. The approach is applied to images of
faces for the purpose of disentangling head-pose from facial expression.
Quantitative metrics show that using Gated-VAE improves average
disentanglement, completeness and informativeness, as compared with un-gated
implementations. Qualitative assessment of latent traversals demonstrate its
disentanglement of head-pose from expression, even when only weak/noisy
supervision is available
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