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Bi-Real Net: Enhancing the Performance of 1-bit CNNs With Improved Representational Capability and Advanced Training Algorithm
[article]
2018
arXiv
pre-print
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Consequently, compared to the standard 1-bit CNN, the representational capability of the Bi-Real net is significantly enhanced and the additional cost on computation is negligible. ...
To minimize the performance gap between the 1-bit and real-valued CNN models, we propose a novel model, dubbed Bi-Real net, which connects the real activations (after the 1-bit convolution and/or BatchNorm ...
Compared with the standard 1-bit CNNs, Bi-Real net utilizes a simple short-cut to significantly enhance the representational capability. ...
arXiv:1808.00278v5
fatcat:pq4cgqsokres3pe2iijyfgmzia
Bi-Real Net: Enhancing the Performance of 1-Bit CNNs with Improved Representational Capability and Advanced Training Algorithm
[chapter]
2018
Lecture Notes in Computer Science
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Consequently, compared to the standard 1-bit CNN, the representational capability of the Bi-Real net is significantly enhanced and the additional cost on computation is negligible. ...
To minimize the performance gap between the 1-bit and real-valued CNN models, we propose a novel model, dubbed Bi-Real net, which connects the real activations (after the 1-bit convolution and/or BatchNorm ...
Compared with the standard 1-bit CNNs, Bi-Real net utilizes a simple short-cut to significantly enhance the representational capability. ...
doi:10.1007/978-3-030-01267-0_44
fatcat:m6uxo74whzdn5b25xsixucuzku
Bi-Real Net: Binarizing Deep Network Towards Real-Network Performance
[article]
2019
arXiv
pre-print
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While efficient, the lacking of representational capability and the training difficulty impede 1-bit CNNs from performing as well as real-valued networks. ...
Bi-Real net is also the first to scale up 1-bit CNNs to an ultra-deep network with 152 layers, and achieves 64.5% top-1 accuracy on ImageNet. ...
Zhenyan Wang and Xiaofeng Hu from Huazhong University of Science and Technology for their efforts in implementing Bi-Real net on FPGA and carrying out the on-board speed estimation. ...
arXiv:1811.01335v2
fatcat:qxqcwipzmnhmjhkkqys4mci6si
HARNU-Net: Hierarchical Attention Residual Nested U-Net for Change Detection in Remote Sensing Images
2022
Sensors
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First, the backbone network is composed of a Siamese network and nested U-Net. ...
The existing CD methods focus on the design of feature extraction network, ignoring the strategy fusion and attention enhancement of the extracted features, which will lead to the problems of incomplete ...
Acknowledgments: We are grateful to researchers for creating and providing publicly available datasets.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/s22124626
pmid:35746407
pmcid:PMC9228169
fatcat:3fdqnqaqnbc5zn6z6u56ah5owq
BitFlow-Net: Towards Fully Binarized Convolutional Neural Networks
2019
IEEE Access
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Enhancing the performance of 1-bit CNNs with improved representational
Manuf.-Green Technol., vol. 3, no. 3, pp. 303–310, 2016. ...
In Bi-Real Net, the net- without compromising the network accuracy. ...
doi:10.1109/access.2019.2945488
fatcat:sq7hcsiconamdborwecu35bsmq
BGaitR-Net: Occluded Gait Sequence reconstructionwith temporally constrained model for gait recognition
[article]
2021
arXiv
pre-print
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We improve the state-of-the-art by developing novel deep learning-based algorithms to identify the occluded frames in an input sequence and next reconstruct these occluded frames by exploiting the spatio-temporal ...
This occlusion reconstruction model has been trained using synthetically occluded CASIA-B and OU-ISIR data, and the trained model is termed as Bidirectional Gait Reconstruction Network BGait-R-Net. ...
Acknowledgments The authors would like to thank NVIDIA for supporting their research with a Titan XP GPU. ...
arXiv:2110.09564v1
fatcat:ie7s2tzttfb77ddxjwlhjzsk4q
Forward and Backward Information Retention for Accurate Binary Neural Networks
[article]
2020
arXiv
pre-print
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Although many binarization methods have improved the accuracy of the model by minimizing the quantization error in forward propagation, there remains a noticeable performance gap between the binarized ...
Comprehensive experiments with various network structures on CIFAR-10 and ImageNet datasets manifest that the proposed IR-Net can consistently outperform state-of-the-art quantization methods. ...
TWN [33] and TTQ [62] enhance the representation ability of neural networks with more available quantization points. ...
arXiv:1909.10788v4
fatcat:ze6m43jcwzcilagnmzaefyts3m
A Review of Recent Advances of Binary Neural Networks for Edge Computing
2020
IEEE Journal on Miniaturization for Air and Space Systems
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2020 pre-print arXiv:2011.14824v1 fatcat:pz4jatlir5e2tg7lvothnbd54a
This paper reviews recent advances on binary neural network (BNN) and 1-bit CNN technologies that are well suitable for front-end, edge-based computing. ...
We also introduce applications in the areas of computer vision and speech recognition and discuss future applications for edge computing. ...
Baochang Zhang is the corresponding author who is also with Shenzhen Academy of Aerospace Technology, Shenzhen, China. ...
doi:10.1109/jmass.2020.3034205
fatcat:7z42xfzoznc3zm6lopjczwijua
A Lightweight Method for Vehicle Classification Based on Improved Binarized Convolutional Neural Network
2022
Electronics
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A binarized CNN-based vehicle classification model is constructed, and the weights and activation values of the model are quantified to 1 bit, which saves data storage space and improves classification ...
The proposed binarized model performs well on the BIT-Vehicle dataset and outperforms some full-precision models. ...
Acknowledgments: We thank our lab teachers and students for their support in the work of this paper.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/electronics11121852
doaj:5b01fb34c17c4fe39e94c520396b3057
fatcat:oceopm6i35hzxbdejmkmyjj6ky
Forward and Backward Information Retention for Accurate Binary Neural Networks
2020
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Although many binarization methods have improved the accuracy of the model by minimizing the quantization error in forward propagation, there remains a noticeable performance gap between the binarized ...
Comprehensive experiments with various network structures on CIFAR-10 and ImageNet datasets manifest that the proposed IR-Net can consistently outperform state-of-the-art quantization methods. ...
TWN [33] and TTQ [62] enhance the representation ability of neural networks with more available quantization points. ...
doi:10.1109/cvpr42600.2020.00232
dblp:conf/cvpr/QinGLSWYS20
fatcat:o72c4gz6sfd3ph5uxmsx2ykdoi
Advances In Video Compression System Using Deep Neural Network: A Review And Case Studies
[article]
2021
arXiv
pre-print
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On post-processing, we demonstrate two neural adaptive filters to respectively facilitate the in-loop and post filtering for the enhancement of compressed frames. ...
quality of experience (QoE) under a limited bit rate budget. ...
We evaluate the performance of our proposed method in terms of bit rate savings and perceived quality. 1) Coding Performance: To evaluate the performance of the proposed switchable texture mode method, ...
arXiv:2101.06341v1
fatcat:63vikavtpnb3dilixbakkcbwnq
Handling Black Swan Events in Deep Learning with Diversely Extrapolated Neural Networks
2020
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
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To avoid this pitfall, we introduce DENN, an ensemble approach building a set of Diversely Extrapolated Neural Networks that fits the training data and is able to generalize more diversely when extrapolating ...
By virtue of their expressive power, neural networks (NNs) are well suited to fitting large, complex datasets, yet they are also known to produce similar predictions for points outside the training distribution ...
For K w /K a = 1/1, our method gains 0.5% and 0.2% Top-1 improvement over Bi-Real Net for ResNet-18 and ResNet-34 respectively. Our method still under-performs BONN and RBCN for ResNet-18. ...
doi:10.24963/ijcai.2020/292
dblp:conf/ijcai/HoangDNC20
fatcat:cu74fehxcnes3irt275t36clgi
RoadNet-RT: High Throughput CNN Architecture and SoC Design for Real-Time Road Segmentation
[article]
2021
arXiv
pre-print
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The proposed CNN architecture has been successfully implemented on an FPGA ZCU102 MPSoC platform that achieves the computation capability of 83.05 GOPS. ...
In order to achieve better performance, often more complex structures and advanced operations are incorporated into the neural networks, which results very long inference time. ...
of ASPP with the same size as input, the output of FFM is upsampled 8 times by the bi-linear resize algorithm. ...
arXiv:2006.07644v2
fatcat:gwo5vub4rbbc3fzqnz6xnswj2e
Learning Architectures for Binary Networks
[article]
2020
arXiv
pre-print
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Quantitative analyses demonstrate that our searched architectures outperform the architectures used in state-of-the-art binary networks and outperform or perform on par with state-of-the-art binary networks ...
We show that our proposed method searches architectures with stable training curves despite the quantization error inherent in binary networks. ...
Acknowledgement The authors would like to thank Dr. Mohammad Rastegari at XNOR.AI for valuable comments and training details of XNOR-Net and Dr. ...
arXiv:2002.06963v2
fatcat:edcfsdrygbhuvdvoggmphzeffi
IMPROVING PSNR AND PROCESSING SPEED FOR HEVC USING HYBRID PSO FOR INTRA FRAME PREDICTION
2020
Zenodo
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Compared to the current state of the art algorithms, this scheme is computationally simple and achieves superior reconstructed video quality (12% increase in PSNR compared to existing methods) at less ...
High efficiency video coding (HEVC) is the newest video codec to increase significantly the coding efficiency of its ancestor H.264/Advance Video Coding. ...
that further enhances the performance by reducing the encoding delay and marginally increasing the bit-rate performance. ...
doi:10.5281/zenodo.4013247
fatcat:t4d27nx765dodpwoz5mvkiktmm
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