10,537 research outputs found
Fast, Exact and Multi-Scale Inference for Semantic Image Segmentation with Deep Gaussian CRFs
In this work we propose a structured prediction technique that combines the
virtues of Gaussian Conditional Random Fields (G-CRF) with Deep Learning: (a)
our structured prediction task has a unique global optimum that is obtained
exactly from the solution of a linear system (b) the gradients of our model
parameters are analytically computed using closed form expressions, in contrast
to the memory-demanding contemporary deep structured prediction approaches that
rely on back-propagation-through-time, (c) our pairwise terms do not have to be
simple hand-crafted expressions, as in the line of works building on the
DenseCRF, but can rather be `discovered' from data through deep architectures,
and (d) out system can trained in an end-to-end manner. Building on standard
tools from numerical analysis we develop very efficient algorithms for
inference and learning, as well as a customized technique adapted to the
semantic segmentation task. This efficiency allows us to explore more
sophisticated architectures for structured prediction in deep learning: we
introduce multi-resolution architectures to couple information across scales in
a joint optimization framework, yielding systematic improvements. We
demonstrate the utility of our approach on the challenging VOC PASCAL 2012
image segmentation benchmark, showing substantial improvements over strong
baselines. We make all of our code and experiments available at
{https://github.com/siddharthachandra/gcrf}Comment: Our code is available at https://github.com/siddharthachandra/gcr
Truncated Variational EM for Semi-Supervised Neural Simpletrons
Inference and learning for probabilistic generative networks is often very
challenging and typically prevents scalability to as large networks as used for
deep discriminative approaches. To obtain efficiently trainable, large-scale
and well performing generative networks for semi-supervised learning, we here
combine two recent developments: a neural network reformulation of hierarchical
Poisson mixtures (Neural Simpletrons), and a novel truncated variational EM
approach (TV-EM). TV-EM provides theoretical guarantees for learning in
generative networks, and its application to Neural Simpletrons results in
particularly compact, yet approximately optimal, modifications of learning
equations. If applied to standard benchmarks, we empirically find, that
learning converges in fewer EM iterations, that the complexity per EM iteration
is reduced, and that final likelihood values are higher on average. For the
task of classification on data sets with few labels, learning improvements
result in consistently lower error rates if compared to applications without
truncation. Experiments on the MNIST data set herein allow for comparison to
standard and state-of-the-art models in the semi-supervised setting. Further
experiments on the NIST SD19 data set show the scalability of the approach when
a manifold of additional unlabeled data is available
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