Hyperbolic Binary Neural Network.

IF 10.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE IEEE transactions on neural networks and learning systems Pub Date : 2024-10-31 DOI:10.1109/TNNLS.2024.3485115
Jun Chen, Jingyang Xiang, Tianxin Huang, Xiangrui Zhao, Yong Liu
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Abstract

Binary neural network (BNN) converts full-precision weights and activations into their extreme 1-bit counterparts, making it particularly suitable for deployment on lightweight mobile devices. While BNNs are typically formulated as a constrained optimization problem and optimized in the binarized space, general neural networks are formulated as an unconstrained optimization problem and optimized in the continuous space. This article introduces the hyperbolic BNN (HBNN) by leveraging the framework of hyperbolic geometry to optimize the constrained problem. Specifically, we transform the constrained problem in hyperbolic space into an unconstrained one in Euclidean space using the Riemannian exponential map. On the other hand, we also propose the exponential parametrization cluster (EPC) method, which, compared with the Riemannian exponential map, shrinks the segment domain based on a diffeomorphism. This approach increases the probability of weight flips, thereby maximizing the information gain in BNNs. Experimental results on CIFAR10, CIFAR100, and ImageNet classification datasets with VGGsmall, ResNet18, and ResNet34 models illustrate the superior performance of our HBNN over state-of-the-art methods.

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双曲二元神经网络
二进制神经网络(BNN)将全精度权值和激活度转换为极端的 1 位对应值,因此特别适合部署在轻量级移动设备上。二值神经网络通常被表述为一个受限优化问题,并在二值化空间中进行优化,而一般神经网络则被表述为一个无约束优化问题,并在连续空间中进行优化。本文通过利用双曲几何框架来优化受限问题,介绍了双曲 BNN(HBNN)。具体来说,我们利用黎曼指数图将双曲空间中的有约束问题转化为欧几里得空间中的无约束问题。另一方面,我们还提出了指数参数化簇(EPC)方法,与黎曼指数图相比,该方法基于差分变形缩小了线段域。这种方法提高了权重翻转的概率,从而使 BNN 的信息增益最大化。使用 VGGsmall、ResNet18 和 ResNet34 模型在 CIFAR10、CIFAR100 和 ImageNet 分类数据集上的实验结果表明,我们的 HBNN 性能优于最先进的方法。
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来源期刊
IEEE transactions on neural networks and learning systems
IEEE transactions on neural networks and learning systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
CiteScore
23.80
自引率
9.60%
发文量
2102
审稿时长
3-8 weeks
期刊介绍: The focus of IEEE Transactions on Neural Networks and Learning Systems is to present scholarly articles discussing the theory, design, and applications of neural networks as well as other learning systems. The journal primarily highlights technical and scientific research in this domain.
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