CNN特征可视化相关论文

Learning Deep Features for Discriminative Localization

https://arxiv.org/pdf/1512.04150.pdf

Top-down Neural Attention by Excitation Backprop

https://arxiv.org/pdf/1608.00507.pdf

Grad-CAM:Visual Explanations from Deep Networks via Gradient-based Localization

https://arxiv.org/pdf/1610.02391.pdf https://github.com/ramprs/grad-cam

Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks

https://arxiv.org/pdf/1710.11063.pdf

Tell Me Where to Look: Guided Attention Inference Network

https://arxiv.org/pdf/1802.10171.pdf

CNN Fixations: An unraveling approach to visualize the discriminative image regions

https://arxiv.org/pdf/1708.06670.pdf

LEARNING HOW TO EXPLAIN NEURAL NETWORKS: PATTERNNET AND PATTERNATTRIBUTION

https://arxiv.org/pdf/1705.05598.pdf

Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach

https://arxiv.org/pdf/1703.08448.pdf

Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers

https://arxiv.org/pdf/1604.00825.pdf

On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation

https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0130140&type=printable

Mining Objects: Fully Unsupervised Object Discovery and Localization From a Single Image

https://arxiv.org/pdf/1902.09968.pdf

weakly supervised object detections

C-WSL: Count-guided Weakly Supervised Localization

https://arxiv.org/pdf/1711.05282.pdf

Improved Techniques for the Weakly-Supervised Object Localization

https://arxiv.org/pdf/1802.07888.pdf

ProNet: Learning to Propose Object-specific Boxes for Cascaded Neural Networks

https://arxiv.org/pdf/1511.03776.pdf

Weakly Supervised Region Proposal Network and Object Detection http://openaccess.thecvf.com/content_ECCV_2018/papers/Peng_Tang_Weakly_Supervised_Region_ECCV_2018_paper.pdf

Saliency Guided End-to-End Learning for Weakly Supervised Object Detection

https://www.ijcai.org/proceedings/2017/0285.pdf

Collaborative Learning for Weakly Supervised Object Detection

https://www.ijcai.org/proceedings/2018/0135.pdf

Training object class detectors with click supervision

http://calvin.inf.ed.ac.uk/wp-content/uploads/Publications/papadopoulos17cvpr.pdf

Seed, Expand, Constrain: Three Principles for Weakly-Supervised Image Segmentation

https://arxiv.org/pdf/1603.06098.pdf

Weakly Supervised Instance Segmentation using Class Peak Response

https://arxiv.org/pdf/1804.00880.pdf

博士，担任《Mechanical System and Signal Processing》审稿专家，担任
《中国电机工程学报》优秀审稿专家，《控制与决策》，《系统工程与电子技术》等EI期刊审稿专家，担任《计算机科学》，《电子器件》 ， 《现代制造过程》 ，《船舶工程》 ，《轴承》 ，《工矿自动化》 ，《重庆理工大学学报》 ，《噪声与振动控制》 ，《机械传动》 ，《机械强度》 ，《机械科学与技术》 ，《机床与液压》，《声学技术》，《应用声学》，《石油机械》，《西安工业大学学报》等中文核心审稿专家。
擅长领域：现代信号处理，机器学习，深度学习，数字孪生，时间序列分析，设备缺陷检测、设备异常检测、设备智能故障诊断与健康管理PHM等。