9,319 research outputs found
Truncated Variational Sampling for "Black Box" Optimization of Generative Models
We investigate the optimization of two probabilistic generative models with
binary latent variables using a novel variational EM approach. The approach
distinguishes itself from previous variational approaches by using latent
states as variational parameters. Here we use efficient and general purpose
sampling procedures to vary the latent states, and investigate the "black box"
applicability of the resulting optimization procedure. For general purpose
applicability, samples are drawn from approximate marginal distributions of the
considered generative model as well as from the model's prior distribution. As
such, variational sampling is defined in a generic form, and is directly
executable for a given model. As a proof of concept, we then apply the novel
procedure (A) to Binary Sparse Coding (a model with continuous observables),
and (B) to basic Sigmoid Belief Networks (which are models with binary
observables). Numerical experiments verify that the investigated approach
efficiently as well as effectively increases a variational free energy
objective without requiring any additional analytical steps
SEVEN: Deep Semi-supervised Verification Networks
Verification determines whether two samples belong to the same class or not,
and has important applications such as face and fingerprint verification, where
thousands or millions of categories are present but each category has scarce
labeled examples, presenting two major challenges for existing deep learning
models. We propose a deep semi-supervised model named SEmi-supervised
VErification Network (SEVEN) to address these challenges. The model consists of
two complementary components. The generative component addresses the lack of
supervision within each category by learning general salient structures from a
large amount of data across categories. The discriminative component exploits
the learned general features to mitigate the lack of supervision within
categories, and also directs the generative component to find more informative
structures of the whole data manifold. The two components are tied together in
SEVEN to allow an end-to-end training of the two components. Extensive
experiments on four verification tasks demonstrate that SEVEN significantly
outperforms other state-of-the-art deep semi-supervised techniques when labeled
data are in short supply. Furthermore, SEVEN is competitive with fully
supervised baselines trained with a larger amount of labeled data. It indicates
the importance of the generative component in SEVEN.Comment: 7 pages, 2 figures, accepted to the 2017 International Joint
Conference on Artificial Intelligence (IJCAI-17
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