Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Trans-layer Unsupervised Networks for Representation Learning   Learning features from massive unlabelled data is a vast prevalent topic for
high-level tasks in many machine learning applications. The recent great
improvements on benchmark data sets achieved by increasingly complex
unsupervised learning methods and deep learning models with lots of parameters
usually requires many tedious tricks and much expertise to tune. However,
filters learned by these complex architectures are quite similar to standard
hand-crafted features visually. In this paper, unsupervised learning methods,
such as PCA or auto-encoder, are employed as the building block to learn filter
banks at each layer. The lower layer responses are transferred to the last
layer (trans-layer) to form a more complete representation retaining more
information. In addition, some beneficial methods such as local contrast
normalization and whitening are added to the proposed deep trans-layer networks
to further boost performance. The trans-layer representations are followed by
block histograms with binary encoder schema to learn translation and rotation
invariant representations, which are utilized to do high-level tasks such as
recognition and classification. Compared to traditional deep learning methods,
the implemented feature learning method has much less parameters and is
validated in several typical experiments, such as digit recognition on MNIST
and MNIST variations, object recognition on Caltech 101 dataset and face
verification on LFW dataset. The deep trans-layer unsupervised learning
achieves 99.45% accuracy on MNIST dataset, 67.11% accuracy on 15 samples per
class and 75.98% accuracy on 30 samples per class on Caltech 101 dataset,
87.10% on LFW dataset.

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The categories of this paper are: cond-mat
3
The categories of this paper are: physics
23
The categories of this paper are: solv-int
15
The categories of this paper are: nucl-ex
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The categories of this paper are: hep-ph
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The categories of this paper are: math-ph
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The categories of this paper are: stat
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The categories of this paper are: chao-dyn
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The categories of this paper are: ao-sci
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The categories of this paper are: mtrl-th
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The categories of this paper are: astro-ph
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The categories of this paper are: nlin
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The categories of this paper are: q-bio
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The categories of this paper are: math
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The categories of this paper are: eess
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The categories of this paper are: quant-ph