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Efficient Deep Learning in Network Compression and Acceleration [chapter]

Shiming Ge
2018 Digital Systems  
In this chapter, I will present a comprehensive survey of several advanced approaches for efficient deep learning in network compression and acceleration.  ...  It is important to design or develop efficient methods to support deep learning toward enabling its scalable deployment, particularly for embedded devices such as mobile, Internet of things (IOT), and  ...  Cooperation Project of Institute of Information Engineering at Chinese Academy of Sciences (Y7Z0511101).  ... 
doi:10.5772/intechopen.79562 fatcat:ya65wwhk5neppgxrut5phd42dy

Be Your Own Best Competitor! Multi-Branched Adversarial Knowledge Transfer [article]

Mahdi Ghorbani, Fahimeh Fooladgar, Shohreh Kasaei
2020 arXiv   pre-print
Hence, The proposed ensemble of sub-models is trained against a discriminator model adversarially. Besides, their knowledge is transferred within the ensemble by four different loss functions.  ...  Among them, the pruning and quantizing methods exhibit a critical drop in performances by compressing the model parameters.  ...  To the best of our knowledge, this is the first work to propose ensemble with the adversarial learning in the knowledge distillation paradigm for semantic segmentation task.  ... 
arXiv:2010.04516v1 fatcat:6qyt3fatgvcm5adknq4upgjfjy

Selfie Segmentation in Video Using N-Frames Ensemble

Yong-Woon Kim, Yung-Cheol Byun, Addapalli V. N. Krishna, K. Balachandran
2021 IEEE Access  
segmentation using a model compression technique,” Comput. Vis. [18] X. S. B, X. Tao, H. Gao, C. Zhou, and J.  ...  Prendinger, “Speedup of deep learning ensembles for semantic Vis. Work., pp. 709–712, 2019, doi: 10.1109/ICCVW.2019.00088.  ... 
doi:10.1109/access.2021.3133276 fatcat:ib757ovz4jaa5mpwuiru5673aa

Deep Learning Acceleration Techniques for Real Time Mobile Vision Applications [article]

Gael Kamdem De Teyou
2019 arXiv   pre-print
Deep Learning (DL) has become a crucial technology for Artificial Intelligence (AI).  ...  This paper presents emerging deep learning acceleration techniques that can enable the delivery of real time visual recognition into the hands of end users, anytime and anywhere.  ...  Another fundamental technique for reducing the complexity of deep neural networks is pruning [19] [20] . Early a lot of researchers have been analyzing network pruning to compress CNNs.  ... 
arXiv:1905.03418v2 fatcat:mxtgdesm2fafbjmyuck5jkphpa

2021 Index IEEE Transactions on Image Processing Vol. 30

2021 IEEE Transactions on Image Processing  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination.  ...  Xu, Y., +, TIP 2021 5782-5792 Biological techniques Array signal processing Graph-Theoretic Post-Processing of Segmentation With Application to A Deep Learning-Based Model That Reduces Speed of Sound Aberrations  ... 
doi:10.1109/tip.2022.3142569 fatcat:z26yhwuecbgrnb2czhwjlf73qu

A Survey on Deep Learning Methods for Robot Vision [article]

Javier Ruiz-del-Solar, Patricio Loncomilla, Naiomi Soto
2018 arXiv   pre-print
To achieve this, a comprehensive overview of deep learning and its usage in computer vision is given, that includes a description of the most frequently used neural models and their main application areas  ...  Afterwards, a review of the principal work using deep learning in robot vision is presented, as well as current and future trends related to the use of deep learning in robotics.  ...  A 2.5x speedup is obtained with no loss of accuracy, and a 4.5x speedup with a loss in accuracy of only 1%.  ... 
arXiv:1803.10862v1 fatcat:bkxbwfkuxbfkrck4aafdgt3moy

DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific Visualization [article]

Chaoli Wang, Jun Han
2022 arXiv   pre-print
In this paper, we survey related deep learning (DL) works in SciVis, specifically in the direction of DL4SciVis: designing DL solutions for solving SciVis problems.  ...  The paper concludes with a discussion of the remaining gaps to fill along the discussed dimensions and the grand challenges we need to tackle as a community.  ...  The authors would like to thank the anonymous reviewers for their insightful comments.  ... 
arXiv:2204.06504v1 fatcat:33fc2smtuffwll6pghdffbebi4

Anytime Dense Prediction with Confidence Adaptivity [article]

Zhuang Liu, Zhiqiu Xu, Hung-Ju Wang, Trevor Darrell, Evan Shelhamer
2022 arXiv   pre-print
We evaluate our method on Cityscapes semantic segmentation and MPII human pose estimation: ADP-C enables anytime inference without sacrificing accuracy while also reducing the total FLOPs of its base models  ...  We propose the first unified and end-to-end approach for anytime dense prediction. A cascade of "exits" is attached to the model to make multiple predictions.  ...  [34] learn a hierarchy of models for anytime segmentation, but its multiple models complicate training and testing, and require more memory.  ... 
arXiv:2104.00749v2 fatcat:4oi3vg2eajgw5n6eh4bhehk2yq

BlockDrop: Dynamic Inference Paths in Residual Networks

Zuxuan Wu, Tushar Nagarajan, Abhishek Kumar, Steven Rennie, Larry S. Davis, Kristen Grauman, Rogerio Feris
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
Built upon a ResNet-101 model, our method achieves a speedup of 20% on average, going as high as 36% for some images, while maintaining the same 76.4% top-1 accuracy on ImageNet.  ...  In particular, given a pretrained ResNet, we train a policy network in an associative reinforcement learning setting for the dual reward of utilizing a minimal number of blocks while preserving recognition  ...  Most existing work pursues model compression techniques to speed up a deep network [19, 4, 25, 41, 36, 32, 16, 54, 31] .  ... 
doi:10.1109/cvpr.2018.00919 dblp:conf/cvpr/WuNKRDGF18 fatcat:b4vxf6d3mjgjro3kvf6rznsvoq

Hierarchical Modified Fast R-CNN for Object Detection

Arindam Chaudhuri
2021 Informatica (Ljubljana, Tiskana izd.)  
For large-scale recognition tasks, scalability is done considering conditional execution of fine category classifiers and layer parameters compression.  ...  In object detection there is high degree of skewedness for objects ' visual separability. It is difficult to distinguish object categories which demand dedicated classification.  ...  However, semantic segmentation does not distinguish between multiple objects of same category.  ... 
doi:10.31449/inf.v45i7.3732 fatcat:rsvbpu2iq5abfkkk2nbt5box6m

BlockDrop: Dynamic Inference Paths in Residual Networks [article]

Zuxuan Wu, Tushar Nagarajan, Abhishek Kumar, Steven Rennie, Larry S. Davis, Kristen Grauman, Rogerio Feris
2019 arXiv   pre-print
Built upon a ResNet-101 model, our method achieves a speedup of 20\% on average, going as high as 36\% for some images, while maintaining the same 76.4\% top-1 accuracy on ImageNet.  ...  In particular, given a pretrained ResNet, we train a policy network in an associative reinforcement learning setting for the dual reward of utilizing a minimal number of blocks while preserving recognition  ...  Most existing work pursues model compression techniques to speed up a deep network [19, 4, 25, 41, 36, 32, 16, 54, 31] .  ... 
arXiv:1711.08393v4 fatcat:hrmjqu2p6raqlbhi65bx4z3x2u

Deep Learning Models for Retinal Blood Vessels Segmentation: A Review

Toufique A. Soomro, Ahmed J. Afifi, Lihong Zheng, Shafiullah Soomro, Junbin Gao, Olaf Hellwich, Manoranjan Paul
2019 IEEE Access  
This paper presents a comprehensive review of the principle and application of deep learning in retinal image analysis.  ...  In this paper, we focus on recent advances in deep learning methods for retinal image analysis.  ...  TABLE 1 . 1 Overview of papers using deep learning techniques for retinal image segmentation. All works use CNNs. TABLE 2 . 2 Performance analysis of segmentation model.  ... 
doi:10.1109/access.2019.2920616 fatcat:zcnjfrbnanaxrmfgdoonqs73aa

CLIP-Q: Deep Network Compression Learning by In-parallel Pruning-Quantization

Frederick Tung, Greg Mori
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
However, modern deep networks contain millions of learned weights; a more efficient utilization of computation resources would assist in a variety of deployment scenarios, from embedded platforms with  ...  resource constraints to computing clusters running ensembles of networks.  ...  Acknowledgements This work was supported by the Natural Sciences and Engineering Research Council of Canada.  ... 
doi:10.1109/cvpr.2018.00821 dblp:conf/cvpr/TungM18 fatcat:ooq2o22m7badzn5ch2fik35j6i

Delta Distillation for Efficient Video Processing [article]

Amirhossein Habibian, Haitam Ben Yahia, Davide Abati, Efstratios Gavves, Fatih Porikli
2022 arXiv   pre-print
for semantic segmentation and object detection in videos.  ...  Finally, we show that, as a by-product, delta distillation improves the temporal consistency of the teacher model.  ...  We first train the teacher networks F using a SGD optimizer with a learning rate of 0.01 for 7 epochs. The learning rate is reduced by a factor of 10 at epochs 2 and 5.  ... 
arXiv:2203.09594v1 fatcat:5t3nwyn45vaxrktwhsgdyoprui

Single-pixel interior filling function approach for detecting and correcting errors in particle tracking

Stanislav Burov, Patrick Figliozzi, Binhua Lin, Stuart A. Rice, Norbert F. Scherer, Aaron R. Dinner
2016 Proceedings of the National Academy of Sciences of the United States of America  
The presented method exploits the symmetry of Hessian matrices, which typically results in a computational speedup of about factor 2 over standard differentiation techniques.  ...  ., Intel's x86 microprocessors) in control applications and signal processing, for example, online implementation of frequency domain iterative learning control (FD-ILC) techniques.  ...  Motivated by these observations, we propose a deep learning framework, which simultaneously performs deep feature learning for visual representation in conjunction with spatiotemporal context modeling.  ... 
doi:10.1073/pnas.1619104114 pmid:28028226 pmcid:PMC5240672 fatcat:imzi4n3bmjguzjakwdalknvkmi
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