Cao, Y.Q.[Yong-Qiang],
Chen, Y.[Yang],
Khosla, D.[Deepak],
Spiking Deep Convolutional Neural Networks for Energy-Efficient Object
Recognition,
IJCV(113), No. 1, May 2015, pp. 54-66.
Springer DOI
1506
BibRef
Sze, V.,
Chen, Y.H.,
Yang, T.J.,
Emer, J.S.,
Efficient Processing of Deep Neural Networks: A Tutorial and Survey,
PIEEE(105), No. 12, December 2017, pp. 2295-2329.
IEEE DOI
1712
Survey, Deep Neural Networks. Artificial intelligence, Benchmark testing,
Biological neural networks, Computer architecture,
spatial architectures
BibRef
Cavigelli, L.,
Benini, L.,
Origami: A 803-GOp/s/W Convolutional Network Accelerator,
CirSysVideo(27), No. 11, November 2017, pp. 2461-2475.
IEEE DOI
1712
Computer architecture, Computer vision, Feature extraction,
Machine learning, Mobile communication, Neural networks,
very large scale integration
BibRef
Ghesu, F.C.[Florin C.],
Krubasik, E.,
Georgescu, B.,
Singh, V.,
Zheng, Y.,
Hornegger, J.[Joachim],
Comaniciu, D.,
Marginal Space Deep Learning: Efficient Architecture for Volumetric
Image Parsing,
MedImg(35), No. 5, May 2016, pp. 1217-1228.
IEEE DOI
1605
Context
BibRef
Revathi, A.R.,
Kumar, D.[Dhananjay],
An efficient system for anomaly detection using deep learning
classifier,
SIViP(11), No. 2, February 2017, pp. 291-299.
WWW Link.
1702
BibRef
Sun, B.,
Feng, H.,
Efficient Compressed Sensing for Wireless Neural Recording:
A Deep Learning Approach,
SPLetters(24), No. 6, June 2017, pp. 863-867.
IEEE DOI
1705
Compressed sensing, Cost function, Dictionaries, Sensors, Training,
Wireless communication, Wireless sensor networks,
Compressed sensing (CS), deep neural network, wireless neural recording
BibRef
Xu, T.B.[Ting-Bing],
Yang, P.[Peipei],
Zhang, X.Y.[Xu-Yao],
Liu, C.L.[Cheng-Lin],
LightweightNet: Toward fast and lightweight convolutional neural
networks via architecture distillation,
PR(88), 2019, pp. 272-284.
Elsevier DOI
1901
Deep network acceleration and compression,
Architecture distillation, Lightweight network
BibRef
Feng, J.[Jie],
Wang, L.[Lin],
Yu, H.[Haipeng],
Jiao, L.C.[Li-Cheng],
Zhang, X.R.[Xiang-Rong],
Divide-and-Conquer Dual-Architecture Convolutional Neural Network for
Classification of Hyperspectral Images,
RS(11), No. 5, 2019, pp. xx-yy.
DOI Link
1903
BibRef
Kim, D.H.[Dae Ha],
Lee, M.K.[Min Kyu],
Lee, S.H.[Seung Hyun],
Song, B.C.[Byung Cheol],
Macro unit-based convolutional neural network for very light-weight
deep learning,
IVC(87), 2019, pp. 68-75.
Elsevier DOI
1906
BibRef
Earlier: A1, A3, A4, Only:
MUNet: Macro Unit-Based Convolutional Neural Network for Mobile
Devices,
EfficientDeep18(1749-17498)
IEEE DOI
1812
Deep neural networks, Light-weight deep learning, Macro-unit.
Convolution, Computational complexity,
Mobile handsets, Neural networks, Performance evaluation
BibRef
Zhang, C.Y.[Chun-Yang],
Zhao, Q.[Qi],
Chen, C.L.P.[C.L. Philip],
Liu, W.X.[Wen-Xi],
Deep compression of probabilistic graphical networks,
PR(96), 2019, pp. 106979.
Elsevier DOI
1909
Deep compression, Probabilistic graphical models,
Probabilistic graphical networks, Deep learning
BibRef
Chen, W.C.[Wei-Chun],
Chang, C.C.[Chia-Che],
Lee, C.R.[Che-Rung],
Knowledge Distillation with Feature Maps for Image Classification,
ACCV18(III:200-215).
Springer DOI
1906
BibRef
Groh, F.[Fabian],
Wieschollek, P.[Patrick],
Lensch, H.P.A.[Hendrik P. A.],
Flex-Convolution,
ACCV18(I:105-122).
Springer DOI
1906
BibRef
Yang, L.[Lu],
Song, Q.[Qing],
Li, Z.X.[Zuo-Xin],
Wu, Y.Q.[Ying-Qi],
Li, X.J.[Xiao-Jie],
Hu, M.J.[Meng-Jie],
Cross Connected Network for Efficient Image Recognition,
ACCV18(I:56-71).
Springer DOI
1906
BibRef
Ignatov, A.[Andrey],
Timofte, R.[Radu],
Chou, W.[William],
Wang, K.[Ke],
Wu, M.[Max],
Van Gool, T.H.L.J.[Tim Hartley Luc J.],
AI Benchmark: Running Deep Neural Networks on Android Smartphones,
PerceptualRest18(V:288-314).
Springer DOI
1905
BibRef
Li, X.,
Zhang, S.,
Jiang, B.,
Qi, Y.,
Chuah, M.C.,
Bi, N.,
DAC: Data-Free Automatic Acceleration of Convolutional Networks,
WACV19(1598-1606)
IEEE DOI
1904
convolutional neural nets, image classification,
Internet of Things, learning (artificial intelligence),
Deep learning
BibRef
He, Y.,
Liu, X.,
Zhong, H.,
Ma, Y.,
AddressNet: Shift-Based Primitives for Efficient Convolutional Neural
Networks,
WACV19(1213-1222)
IEEE DOI
1904
convolutional neural nets, coprocessors,
learning (artificial intelligence), parallel algorithms,
Fuses
BibRef
Singh, P.,
Kadi, V.S.R.,
Verma, N.,
Namboodiri, V.P.,
Stability Based Filter Pruning for Accelerating Deep CNNs,
WACV19(1166-1174)
IEEE DOI
1904
computer networks, graphics processing units,
learning (artificial intelligence), neural nets,
Libraries
BibRef
He, Z.Z.[Zhe-Zhi],
Gong, B.Q.[Bo-Qing],
Fan, D.L.[De-Liang],
Optimize Deep Convolutional Neural Network with Ternarized Weights
and High Accuracy,
WACV19(913-921)
IEEE DOI
1904
reduce to -1, 0, +1.
convolutional neural nets, embedded systems,
image classification, image coding, image representation,
Hardware
BibRef
Bicici, U.C.[Ufuk Can],
Keskin, C.[Cem],
Akarun, L.[Lale],
Conditional Information Gain Networks,
ICPR18(1390-1395)
IEEE DOI
1812
Decision trees, Neural networks, Computational modeling, Training,
Routing, Vegetation, Probability distribution
BibRef
Aldana, R.[Rodrigo],
Campos-Macías, L.[Leobardo],
Zamora, J.[Julio],
Gomez-Gutierrez, D.[David],
Cruz, A.[Adan],
Dynamic Learning Rate for Neural Networks:
A Fixed-Time Stability Approach,
ICPR18(1378-1383)
IEEE DOI
1812
Training, Artificial neural networks, Approximation algorithms,
Optimization, Pattern recognition, Heuristic algorithms, Lyapunov methods
BibRef
Kung, H.T.,
McDanel, B.,
Zhang, S.Q.,
Adaptive Tiling: Applying Fixed-size Systolic Arrays To Sparse
Convolutional Neural Networks,
ICPR18(1006-1011)
IEEE DOI
1812
Sparse matrices, Arrays, Convolution, Adaptive arrays,
Microprocessors, Adaptation models
BibRef
Grelsson, B.,
Felsberg, M.,
Improved Learning in Convolutional Neural Networks with Shifted
Exponential Linear Units (ShELUs),
ICPR18(517-522)
IEEE DOI
1812
convolution, feedforward neural nets, learning (artificial intelligence).
BibRef
Zheng, W.,
Zhang, Z.,
Accelerating the Classification of Very Deep Convolutional Network by
A Cascading Approach,
ICPR18(355-360)
IEEE DOI
1812
computational complexity, convolution, entropy,
feedforward neural nets, image classification,
Measurement uncertainty
BibRef
Manessi, F.,
Rozza, A.,
Bianco, S.,
Napoletano, P.,
Schettini, R.,
Automated Pruning for Deep Neural Network Compression,
ICPR18(657-664)
IEEE DOI
1812
Training, Neural networks, Quantization (signal), Task analysis,
Feature extraction, Pipelines, Image coding
BibRef
Zhong, G.,
Yao, H.,
Zhou, H.,
Merging Neurons for Structure Compression of Deep Networks,
ICPR18(1462-1467)
IEEE DOI
1812
Neurons, Neural networks, Merging, Computer architecture,
Matrix decomposition, Mathematical model, Prototypes
BibRef
Zhou, A.[Aojun],
Yao, A.B.[An-Bang],
Wang, K.[Kuan],
Chen, Y.R.[Yu-Rong],
Explicit Loss-Error-Aware Quantization for Low-Bit Deep Neural
Networks,
CVPR18(9426-9435)
IEEE DOI
1812
Computer vision, Pattern recognition
BibRef
Bhowmik, P.[Pankaj],
Pantho, M.J.H.[M. Jubaer Hossain],
Asadinia, M.[Marjan],
Bobda, C.[Christophe],
Design of a Reconfigurable 3D Pixel-Parallel Neuromorphic
Architecture for Smart Image Sensor,
ECVW18(786-7868)
IEEE DOI
1812
Computer architecture, Image sensors, Visualization,
Program processors, Clocks, Image processing
BibRef
Aggarwal, V.[Vaneet],
Wang, W.L.[Wen-Lin],
Eriksson, B.[Brian],
Sun, Y.F.[Yi-Fan],
Wan, W.Q.[Wen-Qi],
Wide Compression: Tensor Ring Nets,
CVPR18(9329-9338)
IEEE DOI
1812
Neural networks, Image coding, Shape, Merging, Computer architecture
BibRef
Saeedan, F.[Faraz],
Weber, N.[Nicolas],
Goesele, M.[Michael],
Roth, S.[Stefan],
Detail-Preserving Pooling in Deep Networks,
CVPR18(9108-9116)
IEEE DOI
1812
Standards, Visualization, Convolutional neural networks,
Task analysis, Feature extraction, Distortion, Adaptive systems
BibRef
Yu, R.,
Li, A.,
Chen, C.,
Lai, J.,
Morariu, V.I.,
Han, X.,
Gao, M.,
Lin, C.,
Davis, L.S.,
NISP: Pruning Networks Using Neuron Importance Score Propagation,
CVPR18(9194-9203)
IEEE DOI
1812
Neurons, Redundancy, Optimization, Acceleration,
Biological neural networks, Task analysis, Feature extraction
BibRef
Ren, M.[Mengye],
Pokrovsky, A.[Andrei],
Yang, B.[Bin],
Urtasun, R.[Raquel],
SBNet: Sparse Blocks Network for Fast Inference,
CVPR18(8711-8720)
IEEE DOI
1812
Convolution, Kernel, Shape, Object detection, Task analysis
BibRef
Xie, G.T.[Guo-Tian],
Wang, J.D.[Jing-Dong],
Zhang, T.[Ting],
Lai, J.H.[Jian-Huang],
Hong, R.[Richang],
Qi, G.J.[Guo-Jun],
Interleaved Structured Sparse Convolutional Neural Networks,
CVPR18(8847-8856)
IEEE DOI
1812
Convolution, Kernel, Sparse matrices, Redundancy,
Computational modeling, Computer architecture, Computational complexity
BibRef
Kim, E.[Eunwoo],
Ahn, C.[Chanho],
Oh, S.[Songhwai],
NestedNet: Learning Nested Sparse Structures in Deep Neural Networks,
CVPR18(8669-8678)
IEEE DOI
1812
Task analysis, Knowledge engineering, Neural networks,
Computer architecture, Optimization, Redundancy
BibRef
Carreira-Perpinan, M.A.,
Idelbayev, Y.,
'Learning-Compression' Algorithms for Neural Net Pruning,
CVPR18(8532-8541)
IEEE DOI
1812
Neural networks, Optimization, Training, Neurons,
Performance evaluation, Mobile handsets, Quantization (signal)
BibRef
Tung, F.,
Mori, G.,
CLIP-Q: Deep Network Compression Learning by In-parallel
Pruning-Quantization,
CVPR18(7873-7882)
IEEE DOI
1812
Quantization (signal), Training, Neural networks, Visualization,
Image coding, Task analysis, Optimization
BibRef
Bulò, S.R.[Samuel Rota],
Porzi, L.[Lorenzo],
Kontschieder, P.[Peter],
In-place Activated BatchNorm for Memory-Optimized Training of DNNs,
CVPR18(5639-5647)
IEEE DOI
1812
Reduce memory needs.
Training, Buffer storage, Checkpointing, Memory management,
Standards, Semantics
BibRef
Ma, W.,
Wu, Y.,
Wang, Z.,
Wang, G.,
MDCN: Multi-Scale, Deep Inception Convolutional Neural Networks for
Efficient Object Detection,
ICPR18(2510-2515)
IEEE DOI
1812
Feature extraction, Object detection, Computational modeling,
Task analysis, Convolutional neural networks, Hardware, Real-time systems
BibRef
Zhang, D.,
clcNet: Improving the Efficiency of Convolutional Neural Network
Using Channel Local Convolutions,
CVPR18(7912-7919)
IEEE DOI
1812
Kernel, Computational modeling, Computational efficiency,
Convolutional neural networks, Stacking, Computer vision
BibRef
Zhuang, B.,
Shen, C.,
Tan, M.,
Liu, L.,
Reid, I.D.,
Towards Effective Low-Bitwidth Convolutional Neural Networks,
CVPR18(7920-7928)
IEEE DOI
1812
Quantization (signal), Training, Neural networks, Optimization,
Zirconium, Hardware, Convolution
BibRef
Kuen, J.,
Kong, X.,
Lin, Z.,
Wang, G.,
Yin, J.,
See, S.,
Tan, Y.,
Stochastic Downsampling for Cost-Adjustable Inference and Improved
Regularization in Convolutional Networks,
CVPR18(7929-7938)
IEEE DOI
1812
Training, Computational modeling, Computational efficiency,
Stochastic processes, Visualization, Network architecture, Computer vision
BibRef
Shazeer, N.,
Fatahalian, K.,
Mark, W.R.,
Mullapudi, R.T.,
HydraNets: Specialized Dynamic Architectures for Efficient Inference,
CVPR18(8080-8089)
IEEE DOI
1812
Computer architecture, Training, Computational modeling,
Task analysis, Computational efficiency, Optimization, Routing
BibRef
Rebuffi, S.,
Vedaldi, A.,
Bilen, H.,
Efficient Parametrization of Multi-domain Deep Neural Networks,
CVPR18(8119-8127)
IEEE DOI
1812
Task analysis, Neural networks, Adaptation models,
Feature extraction, Visualization, Computational modeling, Standards
BibRef
Cao, S.[Sen],
Liu, Y.Z.[Ya-Zhou],
Zhou, C.X.[Chang-Xin],
Sun, Q.S.[Quan-Sen],
Pongsak, L.S.[La-Sang],
Shen, S.M.[Sheng Mei],
ThinNet: An Efficient Convolutional Neural Network for Object
Detection,
ICPR18(836-841)
IEEE DOI
1812
Convolution, Computational modeling, Object detection,
Neural networks, Computer architecture, Training,
ThinNet
BibRef
Kobayashi, T.,
Analyzing Filters Toward Efficient ConvNet,
CVPR18(5619-5628)
IEEE DOI
1812
Convolution, Feature extraction, Neurons, Image reconstruction,
Visualization, Shape, Computer vision
BibRef
Chou, Y.,
Chan, Y.,
Lee, J.,
Chiu, C.,
Chen, C.,
Merging Deep Neural Networks for Mobile Devices,
EfficientDeep18(1767-17678)
IEEE DOI
1812
Task analysis, Convolution, Merging, Computational modeling,
Neural networks, Kernel, Computer architecture
BibRef
Zhang, Q.,
Zhang, M.,
Wang, M.,
Sui, W.,
Meng, C.,
Yang, J.,
Kong, W.,
Cui, X.,
Lin, W.,
Efficient Deep Learning Inference Based on Model Compression,
EfficientDeep18(1776-17767)
IEEE DOI
1812
Computational modeling, Convolution, Adaptation models,
Image edge detection, Quantization (signal), Kernel
BibRef
Faraone, J.,
Fraser, N.,
Blott, M.,
Leong, P.H.W.,
SYQ: Learning Symmetric Quantization for Efficient Deep Neural
Networks,
CVPR18(4300-4309)
IEEE DOI
1812
Quantization (signal), Hardware, Symmetric matrices, Training,
Complexity theory, Neural networks, Field programmable gate arrays
BibRef
Ma, N.N.[Ning-Ning],
Zhang, X.Y.[Xiang-Yu],
Zheng, H.T.[Hai-Tao],
Sun, J.[Jian],
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture
Design,
ECCV18(XIV: 122-138).
Springer DOI
1810
BibRef
Zhang, X.Y.[Xiang-Yu],
Zhou, X.,
Lin, M.,
Sun, J.,
ShuffleNet: An Extremely Efficient Convolutional Neural Network for
Mobile Devices,
CVPR18(6848-6856)
IEEE DOI
1812
Convolution, Complexity theory, Computer architecture,
Mobile handsets, Computational modeling, Task analysis, Neural networks
BibRef
Prabhu, A.[Ameya],
Varma, G.[Girish],
Namboodiri, A.[Anoop],
Deep Expander Networks: Efficient Deep Networks from Graph Theory,
ECCV18(XIII: 20-36).
Springer DOI
1810
BibRef
Freeman, I.,
Roese-Koerner, L.,
Kummert, A.,
Effnet: An Efficient Structure for Convolutional Neural Networks,
ICIP18(6-10)
IEEE DOI
1809
Convolution, Computational modeling, Optimization, Hardware, Kernel,
Data compression, Convolutional neural networks,
real-time inference
BibRef
Zhou, Z.,
Zhou, W.,
Li, H.,
Hong, R.,
Online Filter Clustering and Pruning for Efficient Convnets,
ICIP18(11-15)
IEEE DOI
1809
Training, Acceleration, Neural networks, Convolution, Tensile stress,
Force, Clustering algorithms, Deep neural networks, similar filter,
cluster loss
BibRef
Elordi, U.[Unai],
Unzueta, L.[Luis],
Arganda-Carreras, I.[Ignacio],
Otaegui, O.[Oihana],
How Can Deep Neural Networks Be Generated Efficiently for Devices with
Limited Resources?,
AMDO18(24-33).
Springer DOI
1807
BibRef
Prabhu, A.[Ameya],
Batchu, V.[Vishal],
Gajawada, R.[Rohit],
Munagala, S.A.[Sri Aurobindo],
Namboodiri, A.[Anoop],
Hybrid Binary Networks: Optimizing for Accuracy, Efficiency and
Memory,
WACV18(821-829)
IEEE DOI
1806
approximation theory, data compression, image classification,
image coding, image representation,
Quantization (signal)
BibRef
Lee, T.K.[Tae Kwan],
Baddar, W.J.[Wissam J.],
Kim, S.T.[Seong Tae],
Ro, Y.M.[Yong Man],
Convolution with Logarithmic Filter Groups for Efficient Shallow CNN,
MMMod18(I:117-129).
Springer DOI
1802
filter grouping in convolution layers.
BibRef
Véniat, T.[Tom],
Denoyer, L.[Ludovic],
Learning Time/Memory-Efficient Deep Architectures with Budgeted Super
Networks,
CVPR18(3492-3500)
IEEE DOI
1812
Computational modeling, Computer architecture,
Stochastic processes, Neural networks, Fabrics, Predictive models, Computer vision
BibRef
Huang, G.[Gao],
Liu, Z.[Zhuang],
van der Maaten, L.[Laurens],
Weinberger, K.Q.[Kilian Q.],
Densely Connected Convolutional Networks,
CVPR17(2261-2269)
IEEE DOI
1711
Award, CVPR. Convolution, Convolutional codes, Network architecture,
Neural networks, Road transportation, Training
BibRef
Huang, G.[Gao],
Sun, Y.[Yu],
Liu, Z.[Zhuang],
Sedra, D.[Daniel],
Weinberger, K.Q.[Kilian Q.],
Deep Networks with Stochastic Depth,
ECCV16(IV: 646-661).
Springer DOI
1611
BibRef
Huang, G.[Gao],
Liu, S.C.[Shi-Chen],
van der Maaten, L.[Laurens],
Weinberger, K.Q.[Kilian Q.],
CondenseNet: An Efficient DenseNet Using Learned Group Convolutions,
CVPR18(2752-2761)
IEEE DOI
1812
CNN on a phone.
Training, Computer architecture, Computational modeling, Standards,
Mobile handsets, Network architecture, Indexes
BibRef
Zhao, G.,
Zhang, Z.,
Guan, H.,
Tang, P.,
Wang, J.,
Rethinking ReLU to Train Better CNNs,
ICPR18(603-608)
IEEE DOI
1812
Convolution, Tensile stress, Network architecture,
Computational efficiency, Computational modeling, Pattern recognition
BibRef
Chan, M.,
Scarafoni, D.,
Duarte, R.,
Thornton, J.,
Skelly, L.,
Learning Network Architectures of Deep CNNs Under Resource
Constraints,
EfficientDeep18(1784-17847)
IEEE DOI
1812
Computer architecture, Computational modeling, Optimization,
Adaptation models, Network architecture, Linear programming, Training
BibRef
Kuen, J.[Jason],
Kong, X.F.[Xiang-Fei],
Wang, G.[Gang],
Tan, Y.P.[Yap-Peng],
DelugeNets: Deep Networks with Efficient and Flexible Cross-Layer
Information Inflows,
CEFR-LCV17(958-966)
IEEE DOI
1802
Complexity theory, Computational modeling,
Convolution, Correlation, Neural networks
BibRef
Singh, A.,
Kingsbury, N.G.,
Efficient Convolutional Network Learning Using Parametric Log Based
Dual-Tree Wavelet ScatterNet,
CEFR-LCV17(1140-1147)
IEEE DOI
1802
Computer architecture, Feature extraction,
Personal area networks, Standards, Training
BibRef
Liu, Z.,
Li, J.,
Shen, Z.,
Huang, G.,
Yan, S.,
Zhang, C.,
Learning Efficient Convolutional Networks through Network Slimming,
ICCV17(2755-2763)
IEEE DOI
1802
convolution, image classification,
learning (artificial intelligence), neural nets, CNNs,
Training
BibRef
Yang, T.J.[Tien-Ju],
Chen, Y.H.[Yu-Hsin],
Sze, V.[Vivienne],
Designing Energy-Efficient Convolutional Neural Networks Using
Energy-Aware Pruning,
CVPR17(6071-6079)
IEEE DOI
1711
Computational modeling, Energy consumption, Estimation, Hardware,
Measurement, Memory management, Smart, phones
BibRef
Ioannou, Y.,
Robertson, D.,
Cipolla, R.,
Criminisi, A.,
Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups,
CVPR17(5977-5986)
IEEE DOI
1711
Computational complexity, Computational modeling,
Computer architecture, Convolution, Graphics processing units,
Neural networks, Training
BibRef
Guo, J.[Jia],
Potkonjak, M.[Miodrag],
Pruning ConvNets Online for Efficient Specialist Models,
ECVW17(430-437)
IEEE DOI
1709
Biological neural networks, Computational modeling,
Computer vision, Convolution, Memory management, Sensitivity, analysis
BibRef
Lin, J.H.,
Xing, T.,
Zhao, R.,
Zhang, Z.,
Srivastava, M.,
Tu, Z.,
Gupta, R.K.,
Binarized Convolutional Neural Networks with Separable Filters for
Efficient Hardware Acceleration,
ECVW17(344-352)
IEEE DOI
1709
Backpropagation, Convolution, Field programmable gate arrays,
Filtering theory, Hardware, Kernel, Training
BibRef
Zhang, X.,
Li, Z.,
Loy, C.C.,
Lin, D.,
PolyNet: A Pursuit of Structural Diversity in Very Deep Networks,
CVPR17(3900-3908)
IEEE DOI
1711
Agriculture, Benchmark testing, Computational efficiency,
Diversity reception, Network architecture, Systematics, Training
BibRef
Yan, S.,
Keynotes: Deep learning for visual understanding:
Effectiveness vs. efficiency,
VCIP16(1-1)
IEEE DOI
1701
BibRef
Karmakar, P.,
Teng, S.W.,
Zhang, D.,
Liu, Y.,
Lu, G.,
Improved Tamura Features for Image Classification Using Kernel Based
Descriptors,
DICTA17(1-7)
IEEE DOI
1804
BibRef
And:
Improved Kernel Descriptors for Effective and Efficient Image
Classification,
DICTA17(1-8)
IEEE DOI
1804
BibRef
Earlier:
Combining Pyramid Match Kernel and Spatial Pyramid for Image
Classification,
DICTA16(1-8)
IEEE DOI
1701
Gabor filters,
image colour analysis, image segmentation.
feature extraction, image classification, image colour analysis,
image representation, effective image classification,
BibRef
Karmakar, P.,
Teng, S.W.,
Lu, G.,
Zhang, D.,
Rotation Invariant Spatial Pyramid Matching for Image Classification,
DICTA15(1-8)
IEEE DOI
1603
image classification
BibRef
Opitz, M.[Michael],
Possegger, H.[Horst],
Bischof, H.[Horst],
Efficient Model Averaging for Deep Neural Networks,
ACCV16(II: 205-220).
Springer DOI
1704
BibRef
Zhang, Z.M.[Zi-Ming],
Chen, Y.T.[Yu-Ting],
Saligrama, V.[Venkatesh],
Efficient Training of Very Deep Neural Networks for Supervised
Hashing,
CVPR16(1487-1495)
IEEE DOI
1612
BibRef
Smith, L.N.,
Cyclical Learning Rates for Training Neural Networks,
WACV17(464-472)
IEEE DOI
1609
Computational efficiency, Computer architecture, Neural networks,
Schedules, Training, Tuning
BibRef
Smith, L.N.,
Hand, E.M.,
Doster, T.,
Gradual DropIn of Layers to Train Very Deep Neural Networks,
CVPR16(4763-4771)
IEEE DOI
1612
BibRef
Pasquet, J.,
Chaumont, M.,
Subsol, G.,
Derras, M.,
Speeding-up a convolutional neural network by connecting an SVM
network,
ICIP16(2286-2290)
IEEE DOI
1610
Computational efficiency
BibRef
Park, W.S.,
Kim, M.,
CNN-based in-loop filtering for coding efficiency improvement,
IVMSP16(1-5)
IEEE DOI
1608
Convolution
BibRef
Moons, B.[Bert],
de Brabandere, B.[Bert],
Van Gool, L.J.[Luc J.],
Verhelst, M.[Marian],
Energy-efficient ConvNets through approximate computing,
WACV16(1-8)
IEEE DOI
1606
Approximation algorithms
BibRef
Hsu, F.C.,
Gubbi, J.,
Palaniswami, M.,
Learning Efficiently- The Deep CNNs-Tree Network,
DICTA15(1-7)
IEEE DOI
1603
learning (artificial intelligence)
BibRef
Highlander, T.[Tyler],
Rodriguez, A.[Andres],
Very Efficient Training of Convolutional Neural Networks using Fast
Fourier Transform and Overlap-and-Add,
BMVC15(xx-yy).
DOI Link
1601
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Forgetting, Explaination, Intrepretation, Understanding of Convolutional Neural Networks .