Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards Accurate Binary Convolutional Neural Network   We introduce a novel scheme to train binary convolutional neural networks
(CNNs) -- CNNs with weights and activations constrained to {-1,+1} at run-time.
It has been known that using binary weights and activations drastically reduce
memory size and accesses, and can replace arithmetic operations with more
efficient bitwise operations, leading to much faster test-time inference and
lower power consumption. However, previous works on binarizing CNNs usually
result in severe prediction accuracy degradation. In this paper, we address
this issue with two major innovations: (1) approximating full-precision weights
with the linear combination of multiple binary weight bases; (2) employing
multiple binary activations to alleviate information loss. The implementation
of the resulting binary CNN, denoted as ABC-Net, is shown to achieve much
closer performance to its full-precision counterpart, and even reach the
comparable prediction accuracy on ImageNet and forest trail datasets, given
adequate binary weight bases and activations.

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The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc
3
The categories of this paper are: physics