Human Pose Estimation 的研究现状能不能给个摘要?

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深度学习应用shuokay 发起了问题 • 1 人关注 • 0 个回复 • 447 次浏览 • 2016-01-25 19:39 • 来自相关话题

微调网络时,对微调的数据有什么具体要求?

深度学习理论辛淼 回复了问题 • 4 人关注 • 1 个回复 • 515 次浏览 • 2016-01-25 13:58 • 来自相关话题

自己写的一个layer(C++),为啥运行起来内存哗哗的往上涨呢?是使用的姿势不对吗?这个layer中的变量全是Blob类型的。。

框架开发使用辛淼 回复了问题 • 2 人关注 • 1 个回复 • 503 次浏览 • 2016-01-23 11:37 • 来自相关话题

faster rcnn中的rpn网络的理解?

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深度学习理论海之蓝 发起了问题 • 5 人关注 • 0 个回复 • 1433 次浏览 • 2016-01-22 14:17 • 来自相关话题

cnn为啥最后一层用softmax而不是其他分类器

深度学习理论ruirui_ICT 回复了问题 • 7 人关注 • 1 个回复 • 1998 次浏览 • 2016-01-21 19:24 • 来自相关话题

用gdb调试caffe,该如何编译caffe

Caffe开发使用王斌_ICT 回复了问题 • 4 人关注 • 1 个回复 • 1173 次浏览 • 2016-01-21 14:28 • 来自相关话题

Solver.cpp中嵌套类callback的作用是什么?

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Caffe开发使用OutLaws 发起了问题 • 1 人关注 • 0 个回复 • 416 次浏览 • 2016-01-20 17:50 • 来自相关话题

momentum的作用是什么?

深度学习理论dnnliu 回复了问题 • 5 人关注 • 2 个回复 • 1120 次浏览 • 2016-01-19 19:17 • 来自相关话题

求问caffe如何实现局部对比度归一化层?

综合问题zhaolixiang 回复了问题 • 2 人关注 • 1 个回复 • 485 次浏览 • 2016-01-19 13:24 • 来自相关话题

caffe怎么提取激励函数层的特征

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Caffe开发使用tarzan 发起了问题 • 1 人关注 • 0 个回复 • 562 次浏览 • 2016-01-19 10:51 • 来自相关话题

Caffe中,Net.hpp中loss_need_backward、blob_need_backward、layer_need_backward这三个变量的作用和它们之间的联系

综合问题OutLaws 回复了问题 • 2 人关注 • 1 个回复 • 592 次浏览 • 2016-01-19 10:29 • 来自相关话题

caffe训练种样本不足问题,怎么用代码批量生成多样本??

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深度学习应用海之蓝 发起了问题 • 4 人关注 • 0 个回复 • 552 次浏览 • 2016-01-18 15:50 • 来自相关话题

CaffeCN推荐阅读论文列表 (持续更新20160131)

论文阅读caffe 发表了文章 • 9 个评论 • 3608 次浏览 • 2016-01-18 11:46 • 来自相关话题

 
CaffeCN推荐阅读论文列表(持续更新中 20160131)

1.理论
1.1 综述
Lecun Y, Bengio Y, Hinton G. Deep learning.[J]. Nature, 2015, 521(7553):436-44.Schmidhuber J. Deep learning in neural networks: An overview[J]. Neural Networks, 2015, 61: 85-117.
1.2 数学基础
K. B. Petersen and M. S. Pedersen, “The matrix cookbook,” nov 2012, Version 20121115.
 
1.3 收敛理论
Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks[C]//International conference on artificial intelligence and statistics. 2010: 249-256.Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift[J]. arXiv preprint arXiv:1502.03167, 2015.Neyshabur B, Salakhutdinov R R, Srebro N. Path-sgd: Path-normalized optimization in deep neural networks[C]//Advances in Neural Information Processing Systems. 2015: 2413-2421.
 
2. 模型
2.1 CNN
Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[C]//Advances in neural information processing systems. 2012: 1097-1105.Szegedy C, Liu W, Jia Y, et al. Going Deeper With Convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015: 1-9.Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv:1409.1556, 2014.Srivastava R K, Greff K, Schmidhuber J. Highway Networks[J]. arXiv preprint arXiv:1505.00387, 2015.

2.2 RNN
Graves A. Long Short-Term Memory[J]. Neural Computation, 1997, 9(8):1735-1780.Graves A, Schmidhuber J. Framewise phoneme classification with bidirectional LSTM and other neural network architectures[J]. Neural Networks, 2005, 18(s 5–6):602-610.Chung J, Gulcehre C, Cho K H, et al. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling[J]. Eprint Arxiv, 2014.

3.应用
 
3.1 图像分类
Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[C]//Advances in neural information processing systems. 2012: 1097-1105.He K, Zhang X, Ren S, et al. Deep Residual Learning for Image Recognition[J]. arXiv preprint arXiv:1512.03385, 2015.Kontschieder P, Fiterau M, Criminisi A, et al. Deep Neural Decision Forests[C]//Proceedings of the IEEE International Conference on Computer Vision. 2015: 1467-1475.(ICCV2015 Marr Prize)Joint Embeddings of Shapes and Images via CNN Image Purification ACM Transactions on Graphics (Proceeding of SIGGRAPH Asia 2015)
 
3.2 人脸识别
Taigman Y, Yang M, Ranzato M, Wolf L. Deepface: Closing the gap to human-level performance in face verification. In: Computer Vision and Pattern Recognition (CVPR). 2014 Sun Y, Wang X, Tang X. Deep learning face representation from predicting 10,000 classes. In: Computer Vision and Pattern Recognition (CVPR). 2014, 1891–1898 Sun Y, Chen Y, Wang X, Tang X. Deep learning face representation by joint identification-verification. In: Advances in Neural Information Processing Systems (NIPS). 2014, 1988–1996Sun Y, Wang X, Tang X. Deeply learned face representations are sparse, selective, and robust. arXiv preprint arXiv:1412.1265, 2014Yi D, Lei Z, Liao S, Li S Z. Learning face representation from scratch. arXiv preprint arXiv:1411.7923, 2014 Schroff F, Kalenichenko D, Philbin J. Facenet: A unified embedding for face recognition and clustering. arXiv preprint arXiv:1503.03832, 2015

3.3 目标检测
Ren S, He K, Girshick R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[C]//Advances in Neural Information Processing Systems. 2015: 91-99.Girshick R. Fast R-CNN[J]. arXiv preprint arXiv:1504.08083, 2015.(ICCV2015)Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on. IEEE, 2014: 580-587.Hosang J, Benenson R, Dollár P, et al. What makes for effective detection proposals[J]. arXiv preprint arXiv:1502.05082, 2015.(TPAMI2015)Yoo D, Park S, Lee J Y, et al. AttentionNet: Aggregating Weak Directions for Accurate Object Detection[C]//Proceedings of the IEEE International Conference on Computer Vision. 2015: 2659-2667.

3.4 OCR
Graves A, Schmidhuber J. Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks.[J]. Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks. - ResearchGate, 2008:545-552.

3.5 图像描述
Donahue J, Hendricks L A, Guadarrama S, et al. Long-term recurrent convolutional networks for visual recognition and description[C]// Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on. IEEE, 2015.

 3.6 动作识别
Simonyan K, Zisserman A. Two-stream convolutional networks for action recognition in videos[C]//Advances in Neural Information Processing Systems. 2014: 568-576.


================================================
说明:本列表由CaffeCN社区(caffecn.cn)答疑组共同整理,仅提供给CaffeCN社区使用,如需转载须注明转载来源。
================================================  查看全部
 
CaffeCN推荐阅读论文列表(持续更新中 20160131)

1.理论
1.1 综述
  • Lecun Y, Bengio Y, Hinton G. Deep learning.[J]. Nature, 2015, 521(7553):436-44.
  • Schmidhuber J. Deep learning in neural networks: An overview[J]. Neural Networks, 2015, 61: 85-117.

1.2 数学基础
  • K. B. Petersen and M. S. Pedersen, “The matrix cookbook,” nov 2012, Version 20121115.

 
1.3 收敛理论
  • Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks[C]//International conference on artificial intelligence and statistics. 2010: 249-256.
  • Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift[J]. arXiv preprint arXiv:1502.03167, 2015.
  • Neyshabur B, Salakhutdinov R R, Srebro N. Path-sgd: Path-normalized optimization in deep neural networks[C]//Advances in Neural Information Processing Systems. 2015: 2413-2421.

 
2. 模型
2.1 CNN
  • Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[C]//Advances in neural information processing systems. 2012: 1097-1105.
  • Szegedy C, Liu W, Jia Y, et al. Going Deeper With Convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015: 1-9.
  • Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv:1409.1556, 2014.
  • Srivastava R K, Greff K, Schmidhuber J. Highway Networks[J]. arXiv preprint arXiv:1505.00387, 2015.


2.2 RNN
  • Graves A. Long Short-Term Memory[J]. Neural Computation, 1997, 9(8):1735-1780.
  • Graves A, Schmidhuber J. Framewise phoneme classification with bidirectional LSTM and other neural network architectures[J]. Neural Networks, 2005, 18(s 5–6):602-610.
  • Chung J, Gulcehre C, Cho K H, et al. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling[J]. Eprint Arxiv, 2014.


3.应用
 
3.1 图像分类
  • Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[C]//Advances in neural information processing systems. 2012: 1097-1105.
  • He K, Zhang X, Ren S, et al. Deep Residual Learning for Image Recognition[J]. arXiv preprint arXiv:1512.03385, 2015.
  • Kontschieder P, Fiterau M, Criminisi A, et al. Deep Neural Decision Forests[C]//Proceedings of the IEEE International Conference on Computer Vision. 2015: 1467-1475.(ICCV2015 Marr Prize)
  • Joint Embeddings of Shapes and Images via CNN Image Purification ACM Transactions on Graphics (Proceeding of SIGGRAPH Asia 2015)

 
3.2 人脸识别
  • Taigman Y, Yang M, Ranzato M, Wolf L. Deepface: Closing the gap to human-level performance in face verification. In: Computer Vision and Pattern Recognition (CVPR). 2014
  •  Sun Y, Wang X, Tang X. Deep learning face representation from predicting 10,000 classes. In: Computer Vision and Pattern Recognition (CVPR). 2014, 1891–1898
  •  Sun Y, Chen Y, Wang X, Tang X. Deep learning face representation by joint identification-verification. In: Advances in Neural Information Processing Systems (NIPS). 2014, 1988–1996
  • Sun Y, Wang X, Tang X. Deeply learned face representations are sparse, selective, and robust. arXiv preprint arXiv:1412.1265, 2014
  • Yi D, Lei Z, Liao S, Li S Z. Learning face representation from scratch. arXiv preprint arXiv:1411.7923, 2014
  •  Schroff F, Kalenichenko D, Philbin J. Facenet: A unified embedding for face recognition and clustering. arXiv preprint arXiv:1503.03832, 2015


3.3 目标检测
  • Ren S, He K, Girshick R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[C]//Advances in Neural Information Processing Systems. 2015: 91-99.
  • Girshick R. Fast R-CNN[J]. arXiv preprint arXiv:1504.08083, 2015.(ICCV2015)
  • Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on. IEEE, 2014: 580-587.
  • Hosang J, Benenson R, Dollár P, et al. What makes for effective detection proposals[J]. arXiv preprint arXiv:1502.05082, 2015.(TPAMI2015)
  • Yoo D, Park S, Lee J Y, et al. AttentionNet: Aggregating Weak Directions for Accurate Object Detection[C]//Proceedings of the IEEE International Conference on Computer Vision. 2015: 2659-2667.


3.4 OCR
  • Graves A, Schmidhuber J. Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks.[J]. Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks. - ResearchGate, 2008:545-552.


3.5 图像描述
  • Donahue J, Hendricks L A, Guadarrama S, et al. Long-term recurrent convolutional networks for visual recognition and description[C]// Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on. IEEE, 2015.


 3.6 动作识别
  • Simonyan K, Zisserman A. Two-stream convolutional networks for action recognition in videos[C]//Advances in Neural Information Processing Systems. 2014: 568-576.



================================================
说明:本列表由CaffeCN社区(caffecn.cn)答疑组共同整理,仅提供给CaffeCN社区使用,如需转载须注明转载来源。
================================================ 

新增lmdb格式的训练样本的方法

深度学习应用薛云峰 回复了问题 • 5 人关注 • 1 个回复 • 715 次浏览 • 2016-01-18 12:04 • 来自相关话题

学习率策略比较

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深度学习理论dnnliu 发起了问题 • 2 人关注 • 0 个回复 • 435 次浏览 • 2016-01-17 23:49 • 来自相关话题

Faster R-CNN ,Rcnn ,fast rcnn与caffe有什么关系呀?它主要是用来检测图像中的多个物体的吗?能否进行人脸比对?

综合问题星空下的巫师 回复了问题 • 10 人关注 • 1 个回复 • 4696 次浏览 • 2016-01-16 10:46 • 来自相关话题

关于message NetState和message NetStateRule的问题

Caffe开发使用孙琳钧 回复了问题 • 4 人关注 • 1 个回复 • 1046 次浏览 • 2016-01-14 23:07 • 来自相关话题

关于Dropout caffe是怎么样实现了

深度学习应用李扬 回复了问题 • 4 人关注 • 2 个回复 • 1140 次浏览 • 2016-01-14 21:50 • 来自相关话题

3D CNN架构中有个hardwired kernels是什马东西?

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深度学习理论smallrock 发起了问题 • 2 人关注 • 0 个回复 • 390 次浏览 • 2016-01-14 20:45 • 来自相关话题