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Optimizing Data Flow in Binary Neural Networks [PDF]
SensorsBinary neural networks (BNNs) can substantially accelerate a neural network’s inference time by substituting its costly floating-point arithmetic with bit-wise operations.
Lorenzo Vorabbi +2 more
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Attention-Based Batch Normalization for Binary Neural Networks [PDF]
EntropyBatch normalization (BN) is crucial for achieving state-of-the-art binary neural networks (BNNs). Unlike full-precision neural networks, BNNs restrict activations to discrete values {−1,1}, which requires a renewed understanding and research of the role ...
Shan Gu +3 more
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Rotation Invariant Local Binary Convolution Neural Networks [PDF]
IEEE Access, 2018 Convolutional neural networks (CNNs) have achieved unprecedented successes in computer vision fields, but they remain challenged by the problem about how to effectively process the orientation transformation of objects with fewer parameters.
Xin Zhang +5 more
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Transfer Learning with Binary Neural Networks [PDF]
, 2017 Previous work has shown that it is possible to train deep neural networks with low precision weights and activations. In the extreme case it is even possible to constrain the network to binary values.
Bohez, Steven +5 more
core +5 more sources
Astrometric Binary Classification via Artificial Neural Networks
The Astrophysical JournalWith nearly two billion stars observed and their corresponding astrometric parameters evaluated in the recent Gaia mission, the number of astrometric binary candidates has risen significantly.
Joe Smith
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Self-distribution binary neural networks [PDF]
Applied Intelligence, 2022 In this work, we study the binary neural networks (BNNs) of which both the weights and activations are binary (i.e., 1-bit representation). Feature representation is critical for deep neural networks, while in BNNs, the features only differ in signs. Prior work introduces scaling factors into binary weights and activations to reduce the quantization ...
Ping Xue +4 more
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BCNN: Binary complex neural network [PDF]
Microprocessors and Microsystems, 2021 Binarized neural networks, or BNNs, show great promise in edge-side applications with resource limited hardware, but raise the concerns of reduced accuracy. Motivated by the complex neural networks, in this paper we introduce complex representation into the BNNs and propose Binary complex neural network -- a novel network design that processes binary ...
Yanfei Li, Tong Geng, Ang Li, Huimin Yu
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Binary Morphological Neural Network
2022 IEEE International Conference on Image Processing (ICIP), 2022 In the last ten years, Convolutional Neural Networks (CNNs) have formed the basis of deep-learning architectures for most computer vision tasks. However, they are not necessarily optimal. For example, mathematical morphology is known to be better suited to deal with binary images.
Aouad, Théodore, Talbot, Hugues
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Binary and Multiclass Text Classification by Means of Separable Convolutional Neural Network
Inventions, 2021 In this paper, the structure of a separable convolutional neural network that consists of an embedding layer, separable convolutional layers, convolutional layer and global average pooling is represented for binary and multiclass text classifications ...
Elena Solovyeva, Ali Abdullah
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Binary Graph Neural Networks [PDF]
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 CVPR 2021 Camera-Ready ...
Bahri, Mehdi +2 more
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