11,371 research outputs found
An Information-theoretical Approach to Semi-supervised Learning under Covariate-shift
A common assumption in semi-supervised learning is that the labeled,
unlabeled, and test data are drawn from the same distribution. However, this
assumption is not satisfied in many applications. In many scenarios, the data
is collected sequentially (e.g., healthcare) and the distribution of the data
may change over time often exhibiting so-called covariate shifts. In this
paper, we propose an approach for semi-supervised learning algorithms that is
capable of addressing this issue. Our framework also recovers some popular
methods, including entropy minimization and pseudo-labeling. We provide new
information-theoretical based generalization error upper bounds inspired by our
novel framework. Our bounds are applicable to both general semi-supervised
learning and the covariate-shift scenario. Finally, we show numerically that
our method outperforms previous approaches proposed for semi-supervised
learning under the covariate shift.Comment: Accepted at AISTATS 202
Adaptive Semi-supervised Learning for Cross-domain Sentiment Classification
We consider the cross-domain sentiment classification problem, where a
sentiment classifier is to be learned from a source domain and to be
generalized to a target domain. Our approach explicitly minimizes the distance
between the source and the target instances in an embedded feature space. With
the difference between source and target minimized, we then exploit additional
information from the target domain by consolidating the idea of semi-supervised
learning, for which, we jointly employ two regularizations -- entropy
minimization and self-ensemble bootstrapping -- to incorporate the unlabeled
target data for classifier refinement. Our experimental results demonstrate
that the proposed approach can better leverage unlabeled data from the target
domain and achieve substantial improvements over baseline methods in various
experimental settings.Comment: Accepted to EMNLP201
Mutual Exclusivity Loss for Semi-Supervised Deep Learning
In this paper we consider the problem of semi-supervised learning with deep
Convolutional Neural Networks (ConvNets). Semi-supervised learning is motivated
on the observation that unlabeled data is cheap and can be used to improve the
accuracy of classifiers. In this paper we propose an unsupervised
regularization term that explicitly forces the classifier's prediction for
multiple classes to be mutually-exclusive and effectively guides the decision
boundary to lie on the low density space between the manifolds corresponding to
different classes of data. Our proposed approach is general and can be used
with any backpropagation-based learning method. We show through different
experiments that our method can improve the object recognition performance of
ConvNets using unlabeled data.Comment: 5 pages, 1 figures, ICIP 201
Input and Weight Space Smoothing for Semi-supervised Learning
We propose regularizing the empirical loss for semi-supervised learning by
acting on both the input (data) space, and the weight (parameter) space. We
show that the two are not equivalent, and in fact are complementary, one
affecting the minimality of the resulting representation, the other
insensitivity to nuisance variability. We propose a method to perform such
smoothing, which combines known input-space smoothing with a novel weight-space
smoothing, based on a min-max (adversarial) optimization. The resulting
Adversarial Block Coordinate Descent (ABCD) algorithm performs gradient ascent
with a small learning rate for a random subset of the weights, and standard
gradient descent on the remaining weights in the same mini-batch. It achieves
comparable performance to the state-of-the-art without resorting to heavy data
augmentation, using a relatively simple architecture
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