Classification
Architecture
- ImageNet Classification with Deep Convolutional Neural Networks. (NIPS,2012).
- Natural Neural Networks. (NIPS,2015).
- Very Deep Convolutional Networks for Large-Scale Image Recognition. (ICLR,2015), project.
- Going deeper with convolutions. (CVPR,2015).
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. (ICML,2015).
- Rethinking the Inception Architecture for Computer Vision. (CVPR,2016).
- Deep Residual Learning for Image Recognition. (CVPR,2016).
Optimization / Regularization
- Taking the human out of the loop: A review of bayesian optimization (2016), B. Shahriari et al. [pdf]
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (2015), S. Loffe and C. Szegedy [pdf] :sparkles:
- Delving deep into rectifiers: Surpassing human-level performance on imagenet classification (2015), K. He et al. [pdf] :sparkles:
- Dropout: A simple way to prevent neural networks from overfitting (2014), N. Srivastava et al. (Hinton) [pdf] :sparkles:
- Adam: A method for stochastic optimization (2014), D. Kingma and J. Ba [pdf]
- Spatial pyramid pooling in deep convolutional networks for visual recognition (2014), K. He et al. [pdf]
- Regularization of neural networks using dropconnect (2013), L. Wan et al. (LeCun) [pdf]
- Improving neural networks by preventing co-adaptation of feature detectors (2012), G. Hinton et al. [pdf] :sparkles:
- Random search for hyper-parameter optimization (2012) J. Bergstra and Y. Bengio [pdf]
Multi-label
Detection
- R-FCN. (NIPS,2016).
- Faster R-CNN. (NIPS,2015).
- Fast R-CNN. (ICCV,2015).
- R-CNN. (CVPR,2014).
Segmentation
Supervised
- Learning Deconvolution Network for Semantic Segmentation. (ICCV,2015), project, homepage.
- Fully Convolutional Networks for Semantic Segmentation. (CVPR best paper honorable mention,2015), Caffe_code, homepage.
Weakly-supervised
- BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation. (ICCV,2015).
- ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation. (CVPR,2016).
- Weakly- and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation. (ICCV,2015)
- What is a Good Image Segment? A Unified Approach to Segment Extraction. (ECCV,2008), project, homepage.
本作品采用知识共享署名 2.5 中国大陆许可协议进行许可，欢迎转载，但转载请注明来自 Sunshine 并保持转载后文章内容的完整。本人保留所有版权相关权利。
