yingdizhang / reading-list-1

Reading list on deep learning

Reading-List

Reading list on deep learning.

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Basic Network and Techniques

• AlexNet: MLA Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems. 2012. ⭐⭐⭐⭐⭐
• Dropout: Srivastava, Nitish, et al. "Dropout: a simple way to prevent neural networks from overfitting." Journal of Machine Learning Research 15.1 (2014): 1929-1958. ⭐⭐⭐⭐
• VGG: Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014). ⭐⭐⭐⭐⭐
• GoogLeNet: Szegedy, Christian, et al. "Going deeper with convolutions." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015. ⭐⭐⭐⭐⭐
• Batch Normalization: Ioffe, Sergey, and Christian Szegedy. "Batch normalization: Accelerating deep network training by reducing internal covariate shift." arXiv preprint arXiv:1502.03167 (2015). [Inception v2] ⭐⭐⭐⭐⭐
• PReLU & msra Initilization: He, Kaiming, et al. "Delving deep into rectifiers: Surpassing human-level performance on imagenet classification." Proceedings of the IEEE international conference on computer vision. 2015. ⭐⭐⭐⭐⭐
• InceptionV3: Szegedy, Christian, et al. "Rethinking the inception architecture for computer vision." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016. ⭐⭐⭐⭐
• ResNet: He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016. ⭐⭐⭐⭐⭐
• Identity ResNet: He, Kaiming, et al. "Identity mappings in deep residual networks." European Conference on Computer Vision. Springer International Publishing, 2016. ⭐⭐⭐⭐⭐
• CReLU: Shang, Wenling, et al. "Understanding and improving convolutional neural networks via concatenated rectified linear units." Proceedings of the International Conference on Machine Learning (ICML). 2016. ⭐⭐⭐
• InceptionV4 & Inception-ResNet: Szegedy, Christian, et al. "Inception-v4, inception-resnet and the impact of residual connections on learning." arXiv preprint arXiv:1602.07261 (2016). ⭐⭐⭐⭐
• ResNeXt: Xie, Saining, et al. "Aggregated residual transformations for deep neural networks." arXiv preprint arXiv:1611.05431 (2016). ⭐⭐⭐⭐
• Batch Renormalization: Ioffe, Sergey. "Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models." arXiv preprint arXiv:1702.03275 (2017). ⭐⭐⭐⭐
• Xception: Chollet, François. "Xception: Deep Learning with Depthwise Separable Convolutions." arXiv preprint arXiv:1610.02357 (2016). ⭐⭐⭐
• MobileNets: Howard, Andrew G., et al. "Mobilenets: Efficient convolutional neural networks for mobile vision applications." arXiv preprint arXiv:1704.04861 (2017). ⭐⭐⭐
• DenseNet: Huang, Gao, et al. "Densely connected convolutional networks." arXiv preprint arXiv:1608.06993 (2016). ⭐⭐⭐⭐⭐
• PolyNet: Zhang, Xingcheng, et al. "Polynet: A pursuit of structural diversity in very deep networks." arXiv preprint arXiv:1611.05725 (2016). Slides ⭐⭐⭐⭐
• IRNN: Le, Quoc V., Navdeep Jaitly, and Geoffrey E. Hinton. "A simple way to initialize recurrent networks of rectified linear units." arXiv preprint arXiv:1504.00941 (2015). ⭐⭐⭐
• ReNet: Visin, Francesco, et al. "ReNet: A recurrent neural network based alternative to convolutional networks." arXiv preprint arXiv:1505.00393 (2015). ⭐⭐⭐⭐
• Non-local Neural Network Wang, Xiaolong, Ross Girshick, Abhinav Gupta, and Kaiming He. "Non-local Neural Networks." arXiv preprint arXiv:1711.07971 (2017). ⭐⭐⭐⭐

Object Detection

• Overfeat: Sermanet, Pierre, et al. "Overfeat: Integrated recognition, localization and detection using convolutional networks." arXiv preprint arXiv:1312.6229 (2013). ⭐⭐⭐⭐
• RCNN: Girshick, Ross, et al. "Rich feature hierarchies for accurate object detection and semantic segmentation." Proceedings of the IEEE conference on computer vision and pattern recognition. 2014. ⭐⭐⭐⭐⭐
• SPP: He, Kaiming, et al. "Spatial pyramid pooling in deep convolutional networks for visual recognition." European Conference on Computer Vision. Springer International Publishing, 2014. ⭐⭐⭐⭐⭐
• Fast RCNN: Girshick, Ross. "Fast r-cnn." Proceedings of the IEEE International Conference on Computer Vision. 2015. ⭐⭐⭐⭐⭐
• Faster RCNN: Ren, Shaoqing, et al. "Faster r-cnn: Towards real-time object detection with region proposal networks." Advances in neural information processing systems. 2015. ⭐⭐⭐⭐⭐
• R-CNN minus R: Lenc, Karel, and Andrea Vedaldi. "R-cnn minus r." arXiv preprint arXiv:1506.06981 (2015). ⭐
• End-to-end people detection in crowded scenes: Stewart, Russell, Mykhaylo Andriluka, and Andrew Y. Ng. "End-to-end people detection in crowded scenes." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016. ⭐⭐
• YOLO: Redmon, Joseph, et al. "You only look once: Unified, real-time object detection." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016. ⭐⭐⭐⭐⭐
• ION: Bell, Sean, et al. "Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016. ⭐⭐⭐⭐
• MultiPath: Zagoruyko, Sergey, et al. "A multipath network for object detection." arXiv preprint arXiv:1604.02135 (2016). ⭐⭐⭐
• SSD: Liu, Wei, et al. "SSD: Single shot multibox detector." European Conference on Computer Vision. Springer International Publishing, 2016. ⭐⭐⭐⭐⭐
• OHEM: Shrivastava, Abhinav, Abhinav Gupta, and Ross Girshick. "Training region-based object detectors with online hard example mining." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016. ⭐⭐⭐⭐⭐
• HyperNet: Kong, Tao, et al. "HyperNet: towards accurate region proposal generation and joint object detection." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016. ⭐⭐⭐⭐
• SDP: Yang, Fan, Wongun Choi, and Yuanqing Lin. "Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016. ⭐⭐⭐⭐
• SubCNN: Xiang, Yu, et al. "Subcategory-aware convolutional neural networks for object proposals and detection." Applications of Computer Vision (WACV), 2017 IEEE Winter Conference on. IEEE, 2017. ⭐⭐⭐
• MSCNN: Cai, Zhaowei, et al. "A unified multi-scale deep convolutional neural network for fast object detection." European Conference on Computer Vision. Springer International Publishing, 2016. ⭐⭐⭐⭐
• RFCN: Li, Yi, Kaiming He, and Jian Sun. "R-fcn: Object detection via region-based fully convolutional networks." Advances in Neural Information Processing Systems. 2016. ⭐⭐⭐⭐⭐
• Shallow Network: Ashraf, Khalid, et al. "Shallow networks for high-accuracy road object-detection." arXiv preprint arXiv:1606.01561 (2016). ⭐⭐
• Is Faster R-CNN Doing Well for Pedestrian Detection: Zhang, Liliang, et al. "Is Faster R-CNN Doing Well for Pedestrian Detection?." European Conference on Computer Vision. Springer International Publishing, 2016. ⭐⭐
• GCNN: Najibi, Mahyar, Mohammad Rastegari, and Larry S. Davis. "G-cnn: an iterative grid based object detector." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016. ⭐⭐⭐
• LocNet: Gidaris, Spyros, and Nikos Komodakis. "Locnet: Improving localization accuracy for object detection." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016. ⭐⭐⭐
• PVANet: Kim, Kye-Hyeon, et al. "PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection." arXiv preprint arXiv:1608.08021 (2016). ⭐⭐⭐⭐
• FPN: Lin, Tsung-Yi, et al. "Feature Pyramid Networks for Object Detection." arXiv preprint arXiv:1612.03144 (2016). ⭐⭐⭐⭐⭐
• TDM: Shrivastava, Abhinav, et al. "Beyond Skip Connections: Top-Down Modulation for Object Detection." arXiv preprint arXiv:1612.06851 (2016). ⭐⭐⭐⭐
• YOLO9000: Redmon, Joseph, and Ali Farhadi. "YOLO9000: Better, Faster, Stronger." arXiv preprint arXiv:1612.08242 (2016). ⭐⭐⭐⭐
• Speed/accuracy trade-offs for modern convolutional object detectors: Huang, Jonathan, et al. "Speed/accuracy trade-offs for modern convolutional object detectors." arXiv preprint arXiv:1611.10012 (2016). ⭐⭐
• GDB-Net: Zeng, Xingyu, et al. "Crafting GBD-Net for Object Detection." arXiv preprint arXiv:1610.02579 (2016). Slides ⭐⭐⭐⭐
• WRInception: Lee, Youngwan, et al. "Wide-Residual-Inception Networks for Real-time Object Detection." arXiv preprint arXiv:1702.01243 (2017). ⭐
• DSSD: Fu, Cheng-Yang, et al. "DSSD: Deconvolutional Single Shot Detector." arXiv preprint arXiv:1701.06659 (2017). ⭐⭐⭐⭐
• A-Fast-RCNN (Hard positive generation): Wang, Xiaolong, Abhinav Shrivastava, and Abhinav Gupta. "A-fast-rcnn: Hard positive generation via adversary for object detection." arXiv preprint arXiv:1704.03414 (2017). ⭐⭐⭐ code
• RRC: Ren, Jimmy, et al. "Accurate Single Stage Detector Using Recurrent Rolling Convolution." arXiv preprint arXiv:1704.05776 (2017). ⭐⭐⭐
• Deformable ConvNets: Dai, Jifeng, et al. "Deformable Convolutional Networks." arXiv preprint arXiv:1703.06211 (2017). ⭐⭐⭐⭐
• RSSD: Jeong, Jisoo, Hyojin Park, and Nojun Kwak. "Enhancement of SSD by concatenating feature maps for object detection." arXiv preprint arXiv:1705.09587 (2017). ⭐⭐
• Perceptual GAN: Li, Jianan, et al. "Perceptual Generative Adversarial Networks for Small Object Detection." arXiv preprint arXiv:1706.05274 (2017). ⭐⭐⭐
• RetinaNet (Focal Loss): Tsung-Yi Lin, Priya Goyal, Ross B. Girshick, Kaiming He, and Piotr Dollár. "Focal Loss for Dense Object Detection." In ICCV. 2017. ⭐⭐⭐⭐⭐
• YOLOv3: Redmon, Joseph, and Ali Farhadi. "YOLOv3: An Incremental Improvement." arXiv preprint arXiv:1804.02767 (2018). ⭐⭐⭐

Semantic Segmentation

• FCN: Long, Jonathan, Evan Shelhamer, and Trevor Darrell. "Fully convolutional networks for semantic segmentation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015. ⭐⭐⭐⭐⭐
• Deconvolution Network for Segmentation: Noh, Hyeonwoo, Seunghoon Hong, and Bohyung Han. "Learning deconvolution network for semantic segmentation." Proceedings of the IEEE International Conference on Computer Vision. 2015. ⭐⭐⭐
• U-Net: Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. "U-net: Convolutional networks for biomedical image segmentation." International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, 2015. ⭐⭐⭐⭐⭐
• CRF as RNN: Zheng, Shuai, et al. "Conditional random fields as recurrent neural networks." In ICCV. 2015. ⭐⭐⭐⭐
• MNC: Dai, Jifeng, Kaiming He, and Jian Sun. "Instance-aware semantic segmentation via multi-task network cascades." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016. ⭐⭐⭐⭐⭐
• InstanceFCN: Dai, Jifeng, et al. "Instance-sensitive fully convolutional networks." arXiv preprint arXiv:1603.08678 (2016). ⭐⭐⭐⭐
• FCIS: Li, Yi, et al. "Fully convolutional instance-aware semantic segmentation." arXiv preprint arXiv:1611.07709 (2016). ⭐⭐⭐⭐⭐
• PSPNet: Zhao, Hengshuang, et al. "Pyramid scene parsing network." arXiv preprint arXiv:1612.01105 (2016). ⭐⭐⭐
• Deeplab v1v2: Chen, Liang-Chieh, et al. "Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs." IEEE transactions on pattern analysis and machine intelligence 40.4 (2018): 834-848. ⭐⭐⭐⭐⭐
• Deeplab v3: Chen, Liang-Chieh, et al. "Rethinking atrous convolution for semantic image segmentation." arXiv preprint arXiv:1706.05587 (2017). ⭐⭐⭐
• Deeplab v3+: Chen, Liang-Chieh, et al. "Encoder-decoder with atrous separable convolution for semantic image segmentation." arXiv preprint arXiv:1802.02611 (2018). ⭐⭐⭐
• Mask R-CNN: He, Kaiming, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. "Mask r-cnn." In ICCV. 2017. ⭐⭐⭐⭐⭐
• Learning to Segment Every Thing (Mask^X R-CNN): Hu, Ronghang, Piotr Dollár, Kaiming He, Trevor Darrell, and Ross Girshick. "Learning to Segment Every Thing." arXiv preprint arXiv:1711.10370 (2017). ⭐⭐⭐⭐⭐
• PANet: Liu, Shu, et al. "Path aggregation network for instance segmentation." arXiv preprint arXiv:1803.01534 (2018). ⭐⭐⭐⭐
• Panoptic Segmentation: Kirillov, A., He, K., Girshick, R., Rother, C., & Dollár, P. (2018). Panoptic Segmentation. arXiv preprint arXiv:1801.00868. ⭐⭐⭐⭐

Weakly Supervised

• Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning: Cinbis, Ramazan Gokberk, Jakob Verbeek, and Cordelia Schmid. "Weakly supervised object localization with multi-fold multiple instance learning." IEEE transactions on pattern analysis and machine intelligence 39.1 (2017): 189-203. ⭐⭐⭐
• Weakly Supervised Deep Detection Networks: Bilen, Hakan, and Andrea Vedaldi. "Weakly supervised deep detection networks." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016. ⭐⭐⭐⭐
• Weakly- and Semi-Supervised Learning: Papandreou, George, et al. "Weakly-and semi-supervised learning of a deep convolutional network for semantic image segmentation." Proceedings of the IEEE International Conference on Computer Vision. 2015. ⭐⭐⭐⭐
• Image-level to pixel-level labeling: Pinheiro, Pedro O., and Ronan Collobert. "From image-level to pixel-level labeling with convolutional networks." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.
• Weakly Supervised Localization using Deep Feature Maps: Bency, Archith J., et al. "Weakly supervised localization using deep feature maps." arXiv preprint arXiv:1603.00489 (2016).
• WELDON: Durand, Thibaut, Nicolas Thome, and Matthieu Cord. "Weldon: Weakly supervised learning of deep convolutional neural networks." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016.
• WILDCAT: Durand, Thibaut, et al. "WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation." The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2017.
• SGDL: Lai, Baisheng, and Xiaojin Gong. "Saliency guided dictionary learning for weakly-supervised image parsing." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016.

Unsupervised

• Learning Features by Watching Objects Move: Pathak, Deepak, et al. "Learning Features by Watching Objects Move." arXiv preprint arXiv:1612.06370 (2016). ⭐⭐⭐⭐⭐
• SimGAN: Shrivastava, Ashish, et al. "Learning from simulated and unsupervised images through adversarial training." arXiv preprint arXiv:1612.07828 (2016). ⭐⭐⭐
• OPN: Lee, Hsin-Ying, et al. "Unsupervised Representation Learning by Sorting Sequences." arXiv preprint arXiv:1708.01246 (2017). ⭐⭐⭐
• Transitive Invariance for Self-supervised Visual Representation Learning: Wang, Xiaolong, et al. "Transitive Invariance for Self-supervised Visual Representation Learning" Proceedings of the IEEE International Conference on Computer Vision. 2017. ⭐⭐⭐ code
• Omni-Supervised Learning: Radosavovic, I., Dollár, P., Girshick, R., Gkioxari, G., & He, K. Data Distillation: Towards Omni-Supervised Learning. In CVPR, 2018. ⭐⭐⭐⭐⭐

Saliency

• DHSNet: Liu, Nian, and Junwei Han. "Dhsnet: Deep hierarchical saliency network for salient object detection." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016. ⭐⭐⭐⭐
• RFCN: Wang, Linzhao, et al. "Saliency detection with recurrent fully convolutional networks." European Conference on Computer Vision. Springer International Publishing, 2016. ⭐⭐⭐⭐
• RACDNN: Kuen, Jason, Zhenhua Wang, and Gang Wang. "Recurrent attentional networks for saliency detection." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016. ⭐⭐⭐⭐
• NLDF: Luo, Zhiming, et al. "Non-Local Deep Features for Salient Object Detection." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017. ⭐⭐⭐
• DSS: Hou, Qibin, et al. "Deeply supervised salient object detection with short connections." arXiv preprint arXiv:1611.04849 (2016). ⭐⭐⭐⭐
• MSRNet: Li, Guanbin, et al. "Instance-Level Salient Object Segmentation." arXiv preprint arXiv:1704.03604 (2017). ⭐⭐⭐⭐
• Amulet: Zhang, Pingping, et al. "Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection." arXiv preprint arXiv:1708.02001 (2017). ⭐⭐⭐⭐
• UCF: Zhang, Pingping, et al. "Learning Uncertain Convolutional Features for Accurate Saliency Detection." arXiv preprint arXiv:1708.02031 (2017). ⭐⭐⭐⭐
• SRM: Wang, Tiantian, et al. "A Stagewise Refinement Model for Detecting Salient Objects in Images." In ICCV. 2017. ⭐⭐⭐⭐
• S4Net: Fan, Ruochen, et al. "$ S^ 4$ Net: Single Stage Salient-Instance Segmentation." arXiv preprint arXiv:1711.07618 (2017). ⭐⭐⭐⭐⭐
• Deep Edge-Aware Saliency Detection： Zhang, Jing, Yuchao Dai, Fatih Porikli, and Mingyi He. "Deep Edge-Aware Saliency Detection." arXiv preprint arXiv:1708.04366 (2017). ⭐⭐⭐
• Bi-Directional Message Passing Model: Zhang, Lu, et al. "A Bi-Directional Message Passing Model for Salient Object Detection." In CVPR. 2018. ⭐⭐⭐
• PiCANet: Liu, Nian, Junwei Han, and Ming-Hsuan Yang. "PiCANet: Learning Pixel-wise Contextual Attention for Saliency Detection." In CVPR. 2018. ⭐⭐⭐⭐⭐
• Detect Globally, Refine Locally: A Novel Approach to Saliency Detection: Wang, Tiantian, et al. "Detect Globally, Refine Locally: A Novel Approach to Saliency Detection." In CVPR. 2018. ⭐⭐⭐
• PAGRN： Zhang, Xiaoning, et al. "Progressive Attention Guided Recurrent Network for Salient Object Detection." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018. ⭐⭐⭐
• Reverse Attention for Salient Object Detection: Chen, Shuhan, et al. "Reverse Attention for Salient Object Detection." In ECCV, 2018. ⭐⭐

Attention

• SRN: Zhu, Feng, et al. "Learning Spatial Regularization with Image-level Supervisions for Multi-label Image Classification." arXiv preprint arXiv:1702.05891 (2017). ⭐⭐⭐⭐
• Zoom-in-Net: Wang, Zhe, et al. "Zoom-in-Net: Deep Mining Lesions for Diabetic Retinopathy Detection." arXiv preprint arXiv:1706.04372 (2017). ⭐⭐⭐⭐
• Multi-context attention: Chu, Xiao, et al. "Multi-context attention for human pose estimation." arXiv preprint arXiv:1702.07432 (2017). ⭐⭐⭐

Shadow Detection/Removal

• DeshadowNet: Qu, Liangqiong, et al. "DeshadowNet: A Multi-context Embedding Deep Network for Shadow Removal." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017. ⭐⭐⭐
• scGAN: Nguyen, Vu, et al. "Shadow Detection with Conditional Generative Adversarial Networks." In ICCV. 2017. ⭐⭐
• Patched CNN: Hosseinzadeh, Sepideh, Moein Shakeri, and Hong Zhang. "Fast Shadow Detection from a Single Image Using a Patched Convolutional Neural Network." arXiv preprint arXiv:1709.09283 (2017). ⭐
• ST-CGAN: Wang, Jifeng, et al. "Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal." arXiv preprint arXiv:1712.02478 (2017). ⭐⭐ (ISTD dataset)

Image Restoration

• DRRN: Tai, Ying, Jian Yang, and Xiaoming Liu. "Image super-resolution via deep recursive residual network." The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2017. ⭐⭐⭐⭐
• DID-MDN: Zhang, He, and Vishal M. Patel. "Density-aware Single Image De-raining using a Multi-stream Dense Network." arXiv preprint arXiv:1802.07412 (2018). ⭐⭐
• IDN: Hui, Zheng, Xiumei Wang, and Xinbo Gao. "Fast and Accurate Single Image Super-Resolution via Information Distillation Network." In CVPR. 2018. ⭐⭐⭐
• SFT-GAN: Wang, X., Yu, K., Dong, C., & Loy, C. C. (2018). Recovering realistic texture in image super-resolution by deep spatial feature transform. In CVPR. 2018. ⭐⭐⭐
• Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring：Nah, Seungjun, Tae Hyun Kim, and Kyoung Mu Lee. "Deep multi-scale convolutional neural network for dynamic scene deblurring." In CVPR, 2017. ⭐⭐⭐
• Enhanced Deep Residual Networks for Single Image Super-Resolution: Lim, Bee, et al. "Enhanced deep residual networks for single image super-resolution." The CVPR workshops, 2017. ⭐
• AGAN for Raindrop Removal: Qian, Rui, et al. "Attentive Generative Adversarial Network for Raindrop Removal from A Single Image." In CVPR. 2018. ⭐⭐⭐⭐⭐
• DCPDN: Zhang, He, and Vishal M. Patel. "Densely connected pyramid dehazing network." In CVPR, 2018. ⭐⭐⭐
• GFN: Ren, W., Ma, L., Zhang, J., Pan, J., Cao, X., Liu, W., & Yang, M. H. (2018). Gated fusion network for single image dehazing. In CVPR, 2018. ⭐⭐⭐⭐
• SIDCGAN: Li, Runde, et al. "Single Image Dehazing via Conditional Generative Adversarial Network." In CVPR, 2018. ⭐⭐

Image Synthesis

• Let there be Color!: Iizuka, Satoshi, Edgar Simo-Serra, and Hiroshi Ishikawa. "Let there be color!: joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification." ACM Transactions on Graphics (TOG) 35.4 (2016): 110. ⭐⭐⭐⭐⭐
• Colorful Image Colorization: Zhang, Richard, Phillip Isola, and Alexei A. Efros. "Colorful image colorization." European Conference on Computer Vision. Springer, Cham, 2016. ⭐⭐⭐⭐
• Neural Style: Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. "A neural algorithm of artistic style." arXiv preprint arXiv:1508.06576 (2015). ⭐⭐⭐⭐⭐
• Texture Synthesis: Gatys, Leon, Alexander S. Ecker, and Matthias Bethge. "Texture synthesis using convolutional neural networks." Advances in Neural Information Processing Systems. 2015. ⭐⭐⭐⭐
• Semantic Annotation Artwork: Champandard, Alex J. "Semantic style transfer and turning two-bit doodles into fine artworks." arXiv preprint arXiv:1603.01768 (2016). ⭐⭐⭐
• MRC+CNN Image Synthesis: Li, Chuan, and Michael Wand. "Combining markov random fields and convolutional neural networks for image synthesis." In CVPR. 2016. ⭐⭐⭐⭐
• More Experiments on Neural Style: Novak, Roman, and Yaroslav Nikulin. "Improving the neural algorithm of artistic style." arXiv preprint arXiv:1605.04603 (2016). ⭐⭐
• Deep Photo Style Transfer: Luan, Fujun, et al. "Deep photo style transfer." In CVPR. 2017. ⭐⭐⭐⭐⭐

Traditional Method

• Rolling Guidance Filter: Zhang, Q., Shen, X., Xu, L., & Jia, J. Rolling guidance filter. In ECCV, 2018. ⭐⭐⭐⭐⭐

Talk

• G-RMI: Google. (Object Detection) slides
• 2017 CVPR Tutorial: video and slides
• 1st ImageNet and COCO Visual Recognition Challenges Joint Workshop: 2015. link
• 2nd ImageNet and COCO Visual Recognition Challenges Joint Workshop: 2016. link