24 research outputs found
Boosted Generative Models
We propose a novel approach for using unsupervised boosting to create an
ensemble of generative models, where models are trained in sequence to correct
earlier mistakes. Our meta-algorithmic framework can leverage any existing base
learner that permits likelihood evaluation, including recent deep expressive
models. Further, our approach allows the ensemble to include discriminative
models trained to distinguish real data from model-generated data. We show
theoretical conditions under which incorporating a new model in the ensemble
will improve the fit and empirically demonstrate the effectiveness of our
black-box boosting algorithms on density estimation, classification, and sample
generation on benchmark datasets for a wide range of generative models.Comment: AAAI 201
Hetero-Modal Variational Encoder-Decoder for Joint Modality Completion and Segmentation
We propose a new deep learning method for tumour segmentation when dealing
with missing imaging modalities. Instead of producing one network for each
possible subset of observed modalities or using arithmetic operations to
combine feature maps, our hetero-modal variational 3D encoder-decoder
independently embeds all observed modalities into a shared latent
representation. Missing data and tumour segmentation can be then generated from
this embedding. In our scenario, the input is a random subset of modalities. We
demonstrate that the optimisation problem can be seen as a mixture sampling. In
addition to this, we introduce a new network architecture building upon both
the 3D U-Net and the Multi-Modal Variational Auto-Encoder (MVAE). Finally, we
evaluate our method on BraTS2018 using subsets of the imaging modalities as
input. Our model outperforms the current state-of-the-art method for dealing
with missing modalities and achieves similar performance to the subset-specific
equivalent networks.Comment: Accepted at MICCAI 201
Leveraging the Exact Likelihood of Deep Latent Variable Models
Deep latent variable models (DLVMs) combine the approximation abilities of
deep neural networks and the statistical foundations of generative models.
Variational methods are commonly used for inference; however, the exact
likelihood of these models has been largely overlooked. The purpose of this
work is to study the general properties of this quantity and to show how they
can be leveraged in practice. We focus on important inferential problems that
rely on the likelihood: estimation and missing data imputation. First, we
investigate maximum likelihood estimation for DLVMs: in particular, we show
that most unconstrained models used for continuous data have an unbounded
likelihood function. This problematic behaviour is demonstrated to be a source
of mode collapse. We also show how to ensure the existence of maximum
likelihood estimates, and draw useful connections with nonparametric mixture
models. Finally, we describe an algorithm for missing data imputation using the
exact conditional likelihood of a deep latent variable model. On several data
sets, our algorithm consistently and significantly outperforms the usual
imputation scheme used for DLVMs
Break The Spell Of Total Correlation In betaTCVAE
In the absence of artificial labels, the independent and dependent features
in the data are cluttered. How to construct the inductive biases of the model
to flexibly divide and effectively contain features with different complexity
is the main focal point of unsupervised disentangled representation learning.
This paper proposes a new iterative decomposition path of total correlation and
explains the disentangled representation ability of VAE from the perspective of
model capacity allocation. The newly developed objective function combines
latent variable dimensions into joint distribution while relieving the
independence constraints of marginal distributions in combination, leading to
latent variables with a more manipulable prior distribution. The novel model
enables VAE to adjust the parameter capacity to divide dependent and
independent data features flexibly. Experimental results on various datasets
show an interesting relevance between model capacity and the latent variable
grouping size, called the "V"-shaped best ELBO trajectory. Additionally, we
empirically demonstrate that the proposed method obtains better disentangling
performance with reasonable parameter capacity allocation