13,703 research outputs found
Multi-Entity Dependence Learning with Rich Context via Conditional Variational Auto-encoder
Multi-Entity Dependence Learning (MEDL) explores conditional correlations
among multiple entities. The availability of rich contextual information
requires a nimble learning scheme that tightly integrates with deep neural
networks and has the ability to capture correlation structures among
exponentially many outcomes. We propose MEDL_CVAE, which encodes a conditional
multivariate distribution as a generating process. As a result, the variational
lower bound of the joint likelihood can be optimized via a conditional
variational auto-encoder and trained end-to-end on GPUs. Our MEDL_CVAE was
motivated by two real-world applications in computational sustainability: one
studies the spatial correlation among multiple bird species using the eBird
data and the other models multi-dimensional landscape composition and human
footprint in the Amazon rainforest with satellite images. We show that
MEDL_CVAE captures rich dependency structures, scales better than previous
methods, and further improves on the joint likelihood taking advantage of very
large datasets that are beyond the capacity of previous methods.Comment: The first two authors contribute equall
Conditional Restricted Boltzmann Machines for Structured Output Prediction
Conditional Restricted Boltzmann Machines (CRBMs) are rich probabilistic
models that have recently been applied to a wide range of problems, including
collaborative filtering, classification, and modeling motion capture data.
While much progress has been made in training non-conditional RBMs, these
algorithms are not applicable to conditional models and there has been almost
no work on training and generating predictions from conditional RBMs for
structured output problems. We first argue that standard Contrastive
Divergence-based learning may not be suitable for training CRBMs. We then
identify two distinct types of structured output prediction problems and
propose an improved learning algorithm for each. The first problem type is one
where the output space has arbitrary structure but the set of likely output
configurations is relatively small, such as in multi-label classification. The
second problem is one where the output space is arbitrarily structured but
where the output space variability is much greater, such as in image denoising
or pixel labeling. We show that the new learning algorithms can work much
better than Contrastive Divergence on both types of problems
A Deep and Autoregressive Approach for Topic Modeling of Multimodal Data
Topic modeling based on latent Dirichlet allocation (LDA) has been a
framework of choice to deal with multimodal data, such as in image annotation
tasks. Another popular approach to model the multimodal data is through deep
neural networks, such as the deep Boltzmann machine (DBM). Recently, a new type
of topic model called the Document Neural Autoregressive Distribution Estimator
(DocNADE) was proposed and demonstrated state-of-the-art performance for text
document modeling. In this work, we show how to successfully apply and extend
this model to multimodal data, such as simultaneous image classification and
annotation. First, we propose SupDocNADE, a supervised extension of DocNADE,
that increases the discriminative power of the learned hidden topic features
and show how to employ it to learn a joint representation from image visual
words, annotation words and class label information. We test our model on the
LabelMe and UIUC-Sports data sets and show that it compares favorably to other
topic models. Second, we propose a deep extension of our model and provide an
efficient way of training the deep model. Experimental results show that our
deep model outperforms its shallow version and reaches state-of-the-art
performance on the Multimedia Information Retrieval (MIR) Flickr data set.Comment: 24 pages, 10 figures. A version has been accepted by TPAMI on Aug
4th, 2015. Add footnote about how to train the model in practice in Section
5.1. arXiv admin note: substantial text overlap with arXiv:1305.530
Deep Learning for Semantic Part Segmentation with High-Level Guidance
In this work we address the task of segmenting an object into its parts, or
semantic part segmentation. We start by adapting a state-of-the-art semantic
segmentation system to this task, and show that a combination of a
fully-convolutional Deep CNN system coupled with Dense CRF labelling provides
excellent results for a broad range of object categories. Still, this approach
remains agnostic to high-level constraints between object parts. We introduce
such prior information by means of the Restricted Boltzmann Machine, adapted to
our task and train our model in an discriminative fashion, as a hidden CRF,
demonstrating that prior information can yield additional improvements. We also
investigate the performance of our approach ``in the wild'', without
information concerning the objects' bounding boxes, using an object detector to
guide a multi-scale segmentation scheme. We evaluate the performance of our
approach on the Penn-Fudan and LFW datasets for the tasks of pedestrian parsing
and face labelling respectively. We show superior performance with respect to
competitive methods that have been extensively engineered on these benchmarks,
as well as realistic qualitative results on part segmentation, even for
occluded or deformable objects. We also provide quantitative and extensive
qualitative results on three classes from the PASCAL Parts dataset. Finally, we
show that our multi-scale segmentation scheme can boost accuracy, recovering
segmentations for finer parts.Comment: 11 pages (including references), 3 figures, 2 table
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