377 research outputs found
Towards Visually Explaining Variational Autoencoders
Recent advances in Convolutional Neural Network (CNN) model interpretability
have led to impressive progress in visualizing and understanding model
predictions. In particular, gradient-based visual attention methods have driven
much recent effort in using visual attention maps as a means for visual
explanations. A key problem, however, is these methods are designed for
classification and categorization tasks, and their extension to explaining
generative models, e.g. variational autoencoders (VAE) is not trivial. In this
work, we take a step towards bridging this crucial gap, proposing the first
technique to visually explain VAEs by means of gradient-based attention. We
present methods to generate visual attention from the learned latent space, and
also demonstrate such attention explanations serve more than just explaining
VAE predictions. We show how these attention maps can be used to localize
anomalies in images, demonstrating state-of-the-art performance on the MVTec-AD
dataset. We also show how they can be infused into model training, helping
bootstrap the VAE into learning improved latent space disentanglement,
demonstrated on the Dsprites dataset
Towards Disentangled Representations via Variational Sparse Coding
International audienceWe present a framework for learning disentangled representations with variational autoencoders in an unsupervised manner, which explicitly imposes sparsity and interpretability of the latent encodings. Leveraging ideas from Sparse Coding models, we consider the Spike and Slab prior distribution for the latent variables, and a modification of the ELBO, inspired by β-VAE model to enforce decomposability over the latent representation. We run our proposed model in a variety of quantitative and qualitative experiments for MNIST, Fashion-MNIST, CelebA and dSprites datasets, showing that the framework disentangles the latent space in continuous sparse interpretable factors and is competitive with current disentangling models
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