Binary Neural Networks: A Survey

• URL: http://arxiv.org/abs/2004.03333v1
• Date: Tue, 31 Mar 2020 16:47:20 GMT
• Title: Binary Neural Networks: A Survey
• Authors: Haotong Qin, Ruihao Gong, Xianglong Liu, Xiao Bai, Jingkuan Song, Nicu Sebe
• Abstract summary: The binary neural network serves as a promising technique for deploying deep models on resource-limited devices. The binarization inevitably causes severe information loss, and even worse, its discontinuity brings difficulty to the optimization of the deep network. We present a survey of these algorithms, mainly categorized into the native solutions directly conducting binarization, and the optimized ones using techniques like minimizing the quantization error, improving the network loss function, and reducing the gradient error.
• Score: 126.67799882857656
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The binary neural network, largely saving the storage and computation, serves as a promising technique for deploying deep models on resource-limited devices. However, the binarization inevitably causes severe information loss, and even worse, its discontinuity brings difficulty to the optimization of the deep network. To address these issues, a variety of algorithms have been proposed, and achieved satisfying progress in recent years. In this paper, we present a comprehensive survey of these algorithms, mainly categorized into the native solutions directly conducting binarization, and the optimized ones using techniques like minimizing the quantization error, improving the network loss function, and reducing the gradient error. We also investigate other practical aspects of binary neural networks such as the hardware-friendly design and the training tricks. Then, we give the evaluation and discussions on different tasks, including image classification, object detection and semantic segmentation. Finally, the challenges that may be faced in future research are prospected.

Related papers

• Convergence Guarantees of Overparametrized Wide Deep Inverse Prior [1.5362025549031046]
  Inverse Priors is an unsupervised approach to transform a random input into an object whose image under the forward model matches the observation. We provide overparametrization bounds under which such network trained via continuous-time gradient descent will converge exponentially fast with high probability. This work is thus a first step towards a theoretical understanding of overparametrized DIP networks, and more broadly it participates to the theoretical understanding of neural networks in inverse problem settings.
  arXiv  Detail & Related papers  (2023-03-20T16:49:40Z)
• Zonotope Domains for Lagrangian Neural Network Verification [102.13346781220383]
  We decompose the problem of verifying a deep neural network into the verification of many 2-layer neural networks. Our technique yields bounds that improve upon both linear programming and Lagrangian-based verification techniques.
  arXiv  Detail & Related papers  (2022-10-14T19:31:39Z)
• Multigoal-oriented dual-weighted-residual error estimation using deep neural networks [0.0]
  Deep learning is considered as a powerful tool with high flexibility to approximate functions. Our approach is based on a posteriori error estimation in which the adjoint problem is solved for the error localization. An efficient and easy to implement algorithm is developed to obtain a posteriori error estimate for multiple goal functionals.
  arXiv  Detail & Related papers  (2021-12-21T16:59:44Z)
• Non-Gradient Manifold Neural Network [79.44066256794187]
  Deep neural network (DNN) generally takes thousands of iterations to optimize via gradient descent. We propose a novel manifold neural network based on non-gradient optimization.
  arXiv  Detail & Related papers  (2021-06-15T06:39:13Z)
• Topological obstructions in neural networks learning [67.8848058842671]
  We study global properties of the loss gradient function flow. We use topological data analysis of the loss function and its Morse complex to relate local behavior along gradient trajectories with global properties of the loss surface.
  arXiv  Detail & Related papers  (2020-12-31T18:53:25Z)
• Robust error bounds for quantised and pruned neural networks [1.8083503268672914]
  Machine learning algorithms are moving towards decentralisation with the data and algorithms stored, and even trained, locally on devices. The device hardware becomes the main bottleneck for model capability in this set-up, creating a need for slimmed down, more efficient neural networks. A semi-definite program is introduced to bound the worst-case error caused by pruning or quantising a neural network. It is hoped that the computed bounds will provide certainty to the performance of these algorithms when deployed on safety-critical systems.
  arXiv  Detail & Related papers  (2020-11-30T22:19:44Z)
• VINNAS: Variational Inference-based Neural Network Architecture Search [2.685668802278155]
  We present a differentiable variational inference-based NAS method for searching sparse convolutional neural networks. Our method finds diverse network cells, while showing state-of-the-art accuracy with up to almost 2 times fewer non-zero parameters.
  arXiv  Detail & Related papers  (2020-07-12T21:47:35Z)
• Parallelization Techniques for Verifying Neural Networks [52.917845265248744]
  We introduce an algorithm based on the verification problem in an iterative manner and explore two partitioning strategies. We also introduce a highly parallelizable pre-processing algorithm that uses the neuron activation phases to simplify the neural network verification problems.
  arXiv  Detail & Related papers  (2020-04-17T20:21:47Z)
• Exploring the Connection Between Binary and Spiking Neural Networks [1.329054857829016]
  We bridge the recent algorithmic progress in training Binary Neural Networks and Spiking Neural Networks. We show that training Spiking Neural Networks in the extreme quantization regime results in near full precision accuracies on large-scale datasets.
  arXiv  Detail & Related papers  (2020-02-24T03:46:51Z)
• Beyond Dropout: Feature Map Distortion to Regularize Deep Neural Networks [107.77595511218429]
  In this paper, we investigate the empirical Rademacher complexity related to intermediate layers of deep neural networks. We propose a feature distortion method (Disout) for addressing the aforementioned problem. The superiority of the proposed feature map distortion for producing deep neural network with higher testing performance is analyzed and demonstrated.
  arXiv  Detail & Related papers  (2020-02-23T13:59:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.