5,950 research outputs found
Compact Random Feature Maps
Kernel approximation using randomized feature maps has recently gained a lot
of interest. In this work, we identify that previous approaches for polynomial
kernel approximation create maps that are rank deficient, and therefore do not
utilize the capacity of the projected feature space effectively. To address
this challenge, we propose compact random feature maps (CRAFTMaps) to
approximate polynomial kernels more concisely and accurately. We prove the
error bounds of CRAFTMaps demonstrating their superior kernel reconstruction
performance compared to the previous approximation schemes. We show how
structured random matrices can be used to efficiently generate CRAFTMaps, and
present a single-pass algorithm using CRAFTMaps to learn non-linear multi-class
classifiers. We present experiments on multiple standard data-sets with
performance competitive with state-of-the-art results.Comment: 9 page
Asymmetric Feature Maps with Application to Sketch Based Retrieval
We propose a novel concept of asymmetric feature maps (AFM), which allows to
evaluate multiple kernels between a query and database entries without
increasing the memory requirements. To demonstrate the advantages of the AFM
method, we derive a short vector image representation that, due to asymmetric
feature maps, supports efficient scale and translation invariant sketch-based
image retrieval. Unlike most of the short-code based retrieval systems, the
proposed method provides the query localization in the retrieved image. The
efficiency of the search is boosted by approximating a 2D translation search
via trigonometric polynomial of scores by 1D projections. The projections are a
special case of AFM. An order of magnitude speed-up is achieved compared to
traditional trigonometric polynomials. The results are boosted by an
image-based average query expansion, exceeding significantly the state of the
art on standard benchmarks.Comment: CVPR 201
PZnet: Efficient 3D ConvNet Inference on Manycore CPUs
Convolutional nets have been shown to achieve state-of-the-art accuracy in
many biomedical image analysis tasks. Many tasks within biomedical analysis
domain involve analyzing volumetric (3D) data acquired by CT, MRI and
Microscopy acquisition methods. To deploy convolutional nets in practical
working systems, it is important to solve the efficient inference problem.
Namely, one should be able to apply an already-trained convolutional network to
many large images using limited computational resources. In this paper we
present PZnet, a CPU-only engine that can be used to perform inference for a
variety of 3D convolutional net architectures. PZNet outperforms MKL-based CPU
implementations of PyTorch and Tensorflow by more than 3.5x for the popular
U-net architecture. Moreover, for 3D convolutions with low featuremap numbers,
cloud CPU inference with PZnet outperfroms cloud GPU inference in terms of cost
efficiency
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