49,244 research outputs found
Semi-Supervised Deep Learning for Fully Convolutional Networks
Deep learning usually requires large amounts of labeled training data, but
annotating data is costly and tedious. The framework of semi-supervised
learning provides the means to use both labeled data and arbitrary amounts of
unlabeled data for training. Recently, semi-supervised deep learning has been
intensively studied for standard CNN architectures. However, Fully
Convolutional Networks (FCNs) set the state-of-the-art for many image
segmentation tasks. To the best of our knowledge, there is no existing
semi-supervised learning method for such FCNs yet. We lift the concept of
auxiliary manifold embedding for semi-supervised learning to FCNs with the help
of Random Feature Embedding. In our experiments on the challenging task of MS
Lesion Segmentation, we leverage the proposed framework for the purpose of
domain adaptation and report substantial improvements over the baseline model.Comment: 9 pages, 6 figure
Disentangled Variational Auto-Encoder for Semi-supervised Learning
Semi-supervised learning is attracting increasing attention due to the fact
that datasets of many domains lack enough labeled data. Variational
Auto-Encoder (VAE), in particular, has demonstrated the benefits of
semi-supervised learning. The majority of existing semi-supervised VAEs utilize
a classifier to exploit label information, where the parameters of the
classifier are introduced to the VAE. Given the limited labeled data, learning
the parameters for the classifiers may not be an optimal solution for
exploiting label information. Therefore, in this paper, we develop a novel
approach for semi-supervised VAE without classifier. Specifically, we propose a
new model called Semi-supervised Disentangled VAE (SDVAE), which encodes the
input data into disentangled representation and non-interpretable
representation, then the category information is directly utilized to
regularize the disentangled representation via the equality constraint. To
further enhance the feature learning ability of the proposed VAE, we
incorporate reinforcement learning to relieve the lack of data. The dynamic
framework is capable of dealing with both image and text data with its
corresponding encoder and decoder networks. Extensive experiments on image and
text datasets demonstrate the effectiveness of the proposed framework.Comment: 6 figures, 10 pages, Information Sciences 201
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