80,409 research outputs found
Neural Architecture Search by Estimation of Network Structure Distributions
The influence of deep learning is continuously expanding across different
domains, and its new applications are ubiquitous. The question of neural
network design thus increases in importance, as traditional empirical
approaches are reaching their limits. Manual design of network architectures
from scratch relies heavily on trial and error, while using existing pretrained
models can introduce redundancies or vulnerabilities. Automated neural
architecture design is able to overcome these problems, but the most successful
algorithms operate on significantly constrained design spaces, assuming the
target network to consist of identical repeating blocks. While such approach
allows for faster search, it does so at the cost of expressivity. We instead
propose an alternative probabilistic representation of a whole neural network
structure under the assumption of independence between layer types. Our matrix
of probabilities is equivalent to the population of models, but allows for
discovery of structural irregularities, while being simple to interpret and
analyze. We construct an architecture search algorithm, inspired by the
estimation of distribution algorithms, to take advantage of this
representation. The probability matrix is tuned towards generating
high-performance models by repeatedly sampling the architectures and evaluating
the corresponding networks, while gradually increasing the model depth. Our
algorithm is shown to discover non-regular models which cannot be expressed via
blocks, but are competitive both in accuracy and computational cost, while not
utilizing complex dataflows or advanced training techniques, as well as
remaining conceptually simple and highly extensible.Comment: 16 pages, 4 figures, 3 table
Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation
This paper proposes a new hybrid architecture that consists of a deep
Convolutional Network and a Markov Random Field. We show how this architecture
is successfully applied to the challenging problem of articulated human pose
estimation in monocular images. The architecture can exploit structural domain
constraints such as geometric relationships between body joint locations. We
show that joint training of these two model paradigms improves performance and
allows us to significantly outperform existing state-of-the-art techniques
DeMoN: Depth and Motion Network for Learning Monocular Stereo
In this paper we formulate structure from motion as a learning problem. We
train a convolutional network end-to-end to compute depth and camera motion
from successive, unconstrained image pairs. The architecture is composed of
multiple stacked encoder-decoder networks, the core part being an iterative
network that is able to improve its own predictions. The network estimates not
only depth and motion, but additionally surface normals, optical flow between
the images and confidence of the matching. A crucial component of the approach
is a training loss based on spatial relative differences. Compared to
traditional two-frame structure from motion methods, results are more accurate
and more robust. In contrast to the popular depth-from-single-image networks,
DeMoN learns the concept of matching and, thus, better generalizes to
structures not seen during training.Comment: Camera ready version for CVPR 2017. Supplementary material included.
Project page:
http://lmb.informatik.uni-freiburg.de/people/ummenhof/depthmotionnet
HyperVAE: A Minimum Description Length Variational Hyper-Encoding Network
We propose a framework called HyperVAE for encoding distributions of
distributions. When a target distribution is modeled by a VAE, its neural
network parameters \theta is drawn from a distribution p(\theta) which is
modeled by a hyper-level VAE. We propose a variational inference using Gaussian
mixture models to implicitly encode the parameters \theta into a low
dimensional Gaussian distribution. Given a target distribution, we predict the
posterior distribution of the latent code, then use a matrix-network decoder to
generate a posterior distribution q(\theta). HyperVAE can encode the parameters
\theta in full in contrast to common hyper-networks practices, which generate
only the scale and bias vectors as target-network parameters. Thus HyperVAE
preserves much more information about the model for each task in the latent
space. We discuss HyperVAE using the minimum description length (MDL) principle
and show that it helps HyperVAE to generalize. We evaluate HyperVAE in density
estimation tasks, outlier detection and discovery of novel design classes,
demonstrating its efficacy
Scalable Population Synthesis with Deep Generative Modeling
Population synthesis is concerned with the generation of synthetic yet
realistic representations of populations. It is a fundamental problem in the
modeling of transport where the synthetic populations of micro-agents represent
a key input to most agent-based models. In this paper, a new methodological
framework for how to 'grow' pools of micro-agents is presented. The model
framework adopts a deep generative modeling approach from machine learning
based on a Variational Autoencoder (VAE). Compared to the previous population
synthesis approaches, including Iterative Proportional Fitting (IPF), Gibbs
sampling and traditional generative models such as Bayesian Networks or Hidden
Markov Models, the proposed method allows fitting the full joint distribution
for high dimensions. The proposed methodology is compared with a conventional
Gibbs sampler and a Bayesian Network by using a large-scale Danish trip diary.
It is shown that, while these two methods outperform the VAE in the
low-dimensional case, they both suffer from scalability issues when the number
of modeled attributes increases. It is also shown that the Gibbs sampler
essentially replicates the agents from the original sample when the required
conditional distributions are estimated as frequency tables. In contrast, the
VAE allows addressing the problem of sampling zeros by generating agents that
are virtually different from those in the original data but have similar
statistical properties. The presented approach can support agent-based modeling
at all levels by enabling richer synthetic populations with smaller zones and
more detailed individual characteristics.Comment: 27 pages, 15 figures, 4 table
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