1,716 research outputs found
Conditional Random Field Autoencoders for Unsupervised Structured Prediction
We introduce a framework for unsupervised learning of structured predictors
with overlapping, global features. Each input's latent representation is
predicted conditional on the observable data using a feature-rich conditional
random field. Then a reconstruction of the input is (re)generated, conditional
on the latent structure, using models for which maximum likelihood estimation
has a closed-form. Our autoencoder formulation enables efficient learning
without making unrealistic independence assumptions or restricting the kinds of
features that can be used. We illustrate insightful connections to traditional
autoencoders, posterior regularization and multi-view learning. We show
competitive results with instantiations of the model for two canonical NLP
tasks: part-of-speech induction and bitext word alignment, and show that
training our model can be substantially more efficient than comparable
feature-rich baselines
Evidence Transfer for Improving Clustering Tasks Using External Categorical Evidence
In this paper we introduce evidence transfer for clustering, a deep learning
method that can incrementally manipulate the latent representations of an
autoencoder, according to external categorical evidence, in order to improve a
clustering outcome. By evidence transfer we define the process by which the
categorical outcome of an external, auxiliary task is exploited to improve a
primary task, in this case representation learning for clustering. Our proposed
method makes no assumptions regarding the categorical evidence presented, nor
the structure of the latent space. We compare our method, against the baseline
solution by performing k-means clustering before and after its deployment.
Experiments with three different kinds of evidence show that our method
effectively manipulates the latent representations when introduced with real
corresponding evidence, while remaining robust when presented with low quality
evidence
Attributes2Classname: A discriminative model for attribute-based unsupervised zero-shot learning
We propose a novel approach for unsupervised zero-shot learning (ZSL) of
classes based on their names. Most existing unsupervised ZSL methods aim to
learn a model for directly comparing image features and class names. However,
this proves to be a difficult task due to dominance of non-visual semantics in
underlying vector-space embeddings of class names. To address this issue, we
discriminatively learn a word representation such that the similarities between
class and combination of attribute names fall in line with the visual
similarity. Contrary to the traditional zero-shot learning approaches that are
built upon attribute presence, our approach bypasses the laborious
attribute-class relation annotations for unseen classes. In addition, our
proposed approach renders text-only training possible, hence, the training can
be augmented without the need to collect additional image data. The
experimental results show that our method yields state-of-the-art results for
unsupervised ZSL in three benchmark datasets.Comment: To appear at IEEE Int. Conference on Computer Vision (ICCV) 201
Temporal Model Adaptation for Person Re-Identification
Person re-identification is an open and challenging problem in computer
vision. Majority of the efforts have been spent either to design the best
feature representation or to learn the optimal matching metric. Most approaches
have neglected the problem of adapting the selected features or the learned
model over time. To address such a problem, we propose a temporal model
adaptation scheme with human in the loop. We first introduce a
similarity-dissimilarity learning method which can be trained in an incremental
fashion by means of a stochastic alternating directions methods of multipliers
optimization procedure. Then, to achieve temporal adaptation with limited human
effort, we exploit a graph-based approach to present the user only the most
informative probe-gallery matches that should be used to update the model.
Results on three datasets have shown that our approach performs on par or even
better than state-of-the-art approaches while reducing the manual pairwise
labeling effort by about 80%
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