Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards Lossless Binary Convolutional Neural Networks Using Piecewise
  Approximation   Binary Convolutional Neural Networks (CNNs) can significantly reduce the
number of arithmetic operations and the size of memory storage, which makes the
deployment of CNNs on mobile or embedded systems more promising. However, the
accuracy degradation of single and multiple binary CNNs is unacceptable for
modern architectures and large scale datasets like ImageNet. In this paper, we
proposed a Piecewise Approximation (PA) scheme for multiple binary CNNs which
lessens accuracy loss by approximating full precision weights and activations
efficiently and maintains parallelism of bitwise operations to guarantee
efficiency. Unlike previous approaches, the proposed PA scheme segments
piece-wisely the full precision weights and activations, and approximates each
piece with a scaling coefficient. Our implementation on ResNet with different
depths on ImageNet can reduce both Top-1 and Top-5 classification accuracy gap
compared with full precision to approximately 1.0%. Benefited from the
binarization of the downsampling layer, our proposed PA-ResNet50 requires less
memory usage and two times Flops than single binary CNNs with 4 weights and 5
activations bases. The PA scheme can also generalize to other architectures
like DenseNet and MobileNet with similar approximation power as ResNet which is
promising for other tasks using binary convolutions. The code and pretrained
models will be publicly available.

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The categories of this paper are: math
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The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
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The categories of this paper are: ao-sci
35
The categories of this paper are: mtrl-th
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The categories of this paper are: alg-geom
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The categories of this paper are: stat
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-lat
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The categories of this paper are: nlin
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The categories of this paper are: solv-int
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The categories of this paper are: adap-org
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The categories of this paper are: atom-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: chem-ph