2,582 research outputs found
3DQ: Compact Quantized Neural Networks for Volumetric Whole Brain Segmentation
Model architectures have been dramatically increasing in size, improving
performance at the cost of resource requirements. In this paper we propose 3DQ,
a ternary quantization method, applied for the first time to 3D Fully
Convolutional Neural Networks (F-CNNs), enabling 16x model compression while
maintaining performance on par with full precision models. We extensively
evaluate 3DQ on two datasets for the challenging task of whole brain
segmentation. Additionally, we showcase our method's ability to generalize on
two common 3D architectures, namely 3D U-Net and V-Net. Outperforming a variety
of baselines, the proposed method is capable of compressing large 3D models to
a few MBytes, alleviating the storage needs in space critical applications.Comment: Accepted to MICCAI 201
LCNN: Lookup-based Convolutional Neural Network
Porting state of the art deep learning algorithms to resource constrained
compute platforms (e.g. VR, AR, wearables) is extremely challenging. We propose
a fast, compact, and accurate model for convolutional neural networks that
enables efficient learning and inference. We introduce LCNN, a lookup-based
convolutional neural network that encodes convolutions by few lookups to a
dictionary that is trained to cover the space of weights in CNNs. Training LCNN
involves jointly learning a dictionary and a small set of linear combinations.
The size of the dictionary naturally traces a spectrum of trade-offs between
efficiency and accuracy. Our experimental results on ImageNet challenge show
that LCNN can offer 3.2x speedup while achieving 55.1% top-1 accuracy using
AlexNet architecture. Our fastest LCNN offers 37.6x speed up over AlexNet while
maintaining 44.3% top-1 accuracy. LCNN not only offers dramatic speed ups at
inference, but it also enables efficient training. In this paper, we show the
benefits of LCNN in few-shot learning and few-iteration learning, two crucial
aspects of on-device training of deep learning models.Comment: CVPR 1
Graph Laplacian for Image Anomaly Detection
Reed-Xiaoli detector (RXD) is recognized as the benchmark algorithm for image
anomaly detection; however, it presents known limitations, namely the
dependence over the image following a multivariate Gaussian model, the
estimation and inversion of a high-dimensional covariance matrix, and the
inability to effectively include spatial awareness in its evaluation. In this
work, a novel graph-based solution to the image anomaly detection problem is
proposed; leveraging the graph Fourier transform, we are able to overcome some
of RXD's limitations while reducing computational cost at the same time. Tests
over both hyperspectral and medical images, using both synthetic and real
anomalies, prove the proposed technique is able to obtain significant gains
over performance by other algorithms in the state of the art.Comment: Published in Machine Vision and Applications (Springer
MORSE: Semantic-ally Drive-n MORpheme SEgment-er
We present in this paper a novel framework for morpheme segmentation which
uses the morpho-syntactic regularities preserved by word representations, in
addition to orthographic features, to segment words into morphemes. This
framework is the first to consider vocabulary-wide syntactico-semantic
information for this task. We also analyze the deficiencies of available
benchmarking datasets and introduce our own dataset that was created on the
basis of compositionality. We validate our algorithm across datasets and
present state-of-the-art results
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