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Transductive Multi-View Zero-Shot Learning
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Transductive Multi-label Zero-shot Learning
Zero-shot learning has received increasing interest as a means to alleviate
the often prohibitive expense of annotating training data for large scale
recognition problems. These methods have achieved great success via learning
intermediate semantic representations in the form of attributes and more
recently, semantic word vectors. However, they have thus far been constrained
to the single-label case, in contrast to the growing popularity and importance
of more realistic multi-label data. In this paper, for the first time, we
investigate and formalise a general framework for multi-label zero-shot
learning, addressing the unique challenge therein: how to exploit multi-label
correlation at test time with no training data for those classes? In
particular, we propose (1) a multi-output deep regression model to project an
image into a semantic word space, which explicitly exploits the correlations in
the intermediate semantic layer of word vectors; (2) a novel zero-shot learning
algorithm for multi-label data that exploits the unique compositionality
property of semantic word vector representations; and (3) a transductive
learning strategy to enable the regression model learned from seen classes to
generalise well to unseen classes. Our zero-shot learning experiments on a
number of standard multi-label datasets demonstrate that our method outperforms
a variety of baselines.Comment: 12 pages, 6 figures, Accepted to BMVC 2014 (oral
MEG Decoding Across Subjects
Brain decoding is a data analysis paradigm for neuroimaging experiments that
is based on predicting the stimulus presented to the subject from the
concurrent brain activity. In order to make inference at the group level, a
straightforward but sometimes unsuccessful approach is to train a classifier on
the trials of a group of subjects and then to test it on unseen trials from new
subjects. The extreme difficulty is related to the structural and functional
variability across the subjects. We call this approach "decoding across
subjects". In this work, we address the problem of decoding across subjects for
magnetoencephalographic (MEG) experiments and we provide the following
contributions: first, we formally describe the problem and show that it belongs
to a machine learning sub-field called transductive transfer learning (TTL).
Second, we propose to use a simple TTL technique that accounts for the
differences between train data and test data. Third, we propose the use of
ensemble learning, and specifically of stacked generalization, to address the
variability across subjects within train data, with the aim of producing more
stable classifiers. On a face vs. scramble task MEG dataset of 16 subjects, we
compare the standard approach of not modelling the differences across subjects,
to the proposed one of combining TTL and ensemble learning. We show that the
proposed approach is consistently more accurate than the standard one
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