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awesome paper for cnn visualization and weakly supervised object detections

cnn_visualization's Introduction

cnn_visualization

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

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

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

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