287 research outputs found
Learning Multi-Scale Representations for Material Classification
The recent progress in sparse coding and deep learning has made unsupervised
feature learning methods a strong competitor to hand-crafted descriptors. In
computer vision, success stories of learned features have been predominantly
reported for object recognition tasks. In this paper, we investigate if and how
feature learning can be used for material recognition. We propose two
strategies to incorporate scale information into the learning procedure
resulting in a novel multi-scale coding procedure. Our results show that our
learned features for material recognition outperform hand-crafted descriptors
on the FMD and the KTH-TIPS2 material classification benchmarks
Representation Learning: A Review and New Perspectives
The success of machine learning algorithms generally depends on data
representation, and we hypothesize that this is because different
representations can entangle and hide more or less the different explanatory
factors of variation behind the data. Although specific domain knowledge can be
used to help design representations, learning with generic priors can also be
used, and the quest for AI is motivating the design of more powerful
representation-learning algorithms implementing such priors. This paper reviews
recent work in the area of unsupervised feature learning and deep learning,
covering advances in probabilistic models, auto-encoders, manifold learning,
and deep networks. This motivates longer-term unanswered questions about the
appropriate objectives for learning good representations, for computing
representations (i.e., inference), and the geometrical connections between
representation learning, density estimation and manifold learning
Deep Exponential Families
We describe \textit{deep exponential families} (DEFs), a class of latent
variable models that are inspired by the hidden structures used in deep neural
networks. DEFs capture a hierarchy of dependencies between latent variables,
and are easily generalized to many settings through exponential families. We
perform inference using recent "black box" variational inference techniques. We
then evaluate various DEFs on text and combine multiple DEFs into a model for
pairwise recommendation data. In an extensive study, we show that going beyond
one layer improves predictions for DEFs. We demonstrate that DEFs find
interesting exploratory structure in large data sets, and give better
predictive performance than state-of-the-art models
Deep Learning of Representations: Looking Forward
Deep learning research aims at discovering learning algorithms that discover
multiple levels of distributed representations, with higher levels representing
more abstract concepts. Although the study of deep learning has already led to
impressive theoretical results, learning algorithms and breakthrough
experiments, several challenges lie ahead. This paper proposes to examine some
of these challenges, centering on the questions of scaling deep learning
algorithms to much larger models and datasets, reducing optimization
difficulties due to ill-conditioning or local minima, designing more efficient
and powerful inference and sampling procedures, and learning to disentangle the
factors of variation underlying the observed data. It also proposes a few
forward-looking research directions aimed at overcoming these challenges
Zero-bias autoencoders and the benefits of co-adapting features
Regularized training of an autoencoder typically results in hidden unit
biases that take on large negative values. We show that negative biases are a
natural result of using a hidden layer whose responsibility is to both
represent the input data and act as a selection mechanism that ensures sparsity
of the representation. We then show that negative biases impede the learning of
data distributions whose intrinsic dimensionality is high. We also propose a
new activation function that decouples the two roles of the hidden layer and
that allows us to learn representations on data with very high intrinsic
dimensionality, where standard autoencoders typically fail. Since the decoupled
activation function acts like an implicit regularizer, the model can be trained
by minimizing the reconstruction error of training data, without requiring any
additional regularization
Variational Sparse Coding
Unsupervised discovery of interpretable features and controllable generation with highdimensional data are currently major challenges in machine learning, with applications
in data visualisation, clustering and artificial
data synthesis. We propose a model based
on variational auto-encoders (VAEs) in which
interpretation is induced through latent space
sparsity with a mixture of Spike and Slab distributions as prior. We derive an evidence
lower bound for this model and propose a specific training method for recovering disentangled features as sparse elements in latent vectors. In our experiments, we demonstrate superior disentanglement performance to standard
VAE approaches when an estimate of the number of true sources of variation is not available
and objects display different combinations of
attributes. Furthermore, the new model provides unique capabilities, such as recovering
feature exploitation, synthesising samples that
share attributes with a given input object and
controlling both discrete and continuous features upon generation
Variational Sparse Coding
Unsupervised discovery of interpretable features and controllable generation with highdimensional data are currently major challenges in machine learning, with applications
in data visualisation, clustering and artificial
data synthesis. We propose a model based
on variational auto-encoders (VAEs) in which
interpretation is induced through latent space
sparsity with a mixture of Spike and Slab distributions as prior. We derive an evidence
lower bound for this model and propose a specific training method for recovering disentangled features as sparse elements in latent vectors. In our experiments, we demonstrate superior disentanglement performance to standard
VAE approaches when an estimate of the number of true sources of variation is not available
and objects display different combinations of
attributes. Furthermore, the new model provides unique capabilities, such as recovering
feature exploitation, synthesising samples that
share attributes with a given input object and
controlling both discrete and continuous features upon generation
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