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深度学习近年来关于神经网络模型解释性的相关高引用/顶会论文(附带代码)
| Date | Stars |
|---|---|
| 2026-07-24 | 767 |
| 2026-07-25 | 767 |
| 2026-07-28 | 767 |
| 2026-07-30 | 767 |
| 2026-07-31 | 767 |
| 2026-08-06 | 767 |
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# awesome_deep_learning_interpretability 深度学习近年来关于模型解释性的相关论文。 按引用次数排序可见[引用排序](./sort_cite.md) 159篇论文pdf(有2篇需要上scihub找)上传到[腾讯微云](https://share.weiyun.com/5ddB0EQ)。 不定期更新。 |Year|Publication|Paper|Citation|code| |:---:|:---:|:---:|:---:|:---:| |2020|CVPR|[Explaining Knowledge Distillation by Quantifying the Knowledge](https://arxiv.org/pdf/2003.03622.pdf)|81| |2020|CVPR|[High-frequency Component Helps Explain the Generalization of Convolutional Neural Networks](https://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_High-Frequency_Component_Helps_Explain_the_Generalization_of_Convolutional_Neural_Networks_CVPR_2020_paper.pdf)|289| |2020|CVPRW|[Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks](https://openaccess.thecvf.com/content_CVPRW_2020/papers/w1/Wang_Score-CAM_Score-Weighted_Visual_Explanations_for_Convolutional_Neural_Networks_CVPRW_2020_paper.pdf)|414|[Pytorch](https://github.com/haofanwang/Score-CAM) |2020|ICLR|[Knowledge consistency between neural networks and beyond](https://arxiv.org/pdf/1908.01581.pdf)|28| |2020|ICLR|[Interpretable Complex-Valued Neural Networks for Privacy Protection](https://arxiv.org/pdf/1901.09546.pdf)|23| |2019|AI|[Explanation in artificial intelligence: Insights from the social sciences](https://arxiv.org/pdf/1706.07269.pdf)|3248| |2019|NMI|[Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead](https://arxiv.org/pdf/1811.10154.pdf)|3505| |2019|NeurIPS|[Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift](https://papers.nips.cc/paper/9547-can-you-trust-your-models-uncertainty-evaluating-predictive-uncertainty-under-dataset-shift.pdf)|1052|-| |2019|NeurIPS|[This looks like that: deep learning for interpretable image recognition](http://papers.nips.cc/paper/9095-this-looks-like-that-deep-learning-for-interpretable-image-recognition.pdf)|665|[Pytorch](https://github.com/cfchen-duke/ProtoPNet)| |2019|NeurIPS|[A benchmark for interpretability methods in deep neural networks](https://papers.nips.cc/paper/9167-a-benchmark-for-interpretability-methods-in-deep-neural-networks.pdf)|413| |2019|NeurIPS|[Full-gradient representation for neural network visualization](http://papers.nips.cc/paper/8666-full-gradient-representation-for-neural-network-visualization.pdf)|155| |2019|NeurIPS|[On the (In) fidelity and Sensitivity of Explanations](https://papers.nips.cc/paper/9278-on-the-infidelity-and-sensitivity-of-explanations.pdf)|226| |2019|NeurIPS|[Towards Automatic Concept-based Explanations](http://papers.nips.cc/paper/9126-towards-automatic-concept-based-explanations.pdf)|342|[Tensorflow](https://github.com/amiratag/ACE)| |2019|NeurIPS|[CXPlain: Causal explanations for model interpretation under uncertainty](http://papers.nips.cc/paper/9211-cxplain-causal-explanations-for-model-interpretation-under-uncertainty.pdf)|133| |2019|CVPR|[Interpreting CNNs via Decision Trees](http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_Interpreting_CNNs_via_Decision_Trees_CVPR_2019_paper.pdf)|293| |2019|CVPR|[From Recognition to Cognition: Visual Commonsense Reasoning](http://openaccess.thecvf.com/content_CVPR_2019/papers/Zellers_From_Recognition_to_Cognition_Visual_Commonsense_Reasoning_CVPR_2019_paper.pdf)|544|[Pytorch](https://github.com/rowanz/r2c)| |2019|CVPR|[Attention branch network: Learning of attention mechanism for visual explanation](http://openaccess.thecvf.com/content_CVPR_2019/papers/Fukui_Attention_Branch_Network_Learning_of_Attention_Mechanism_for_Visual_Explanation_CVPR_2019_paper.pdf)|371| |2019|CVPR|[Interpretable and fine-grained visual explanations for convolutional neural networks](http://openaccess.thecvf.com/content_CVPR_2019/papers/Wagner_Interpretable_and_Fine-Grained_Visual_Explanations_for_Convolutional_Neural_Networks_CVPR_2019_paper.pdf)|116| |2019|CVPR|[Learning to Explain with Complemental Examples](http://openaccess.thecvf.com/content_CVPR_201
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Megvii Research · China
14
Frank (Haofan) Wang
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:b729075ef52aa6a6, topic:deep-learning, topic:neural-network, topic:pytorch
matched fp:b729075ef52aa6a6, topic:awesome, topic:awesome-list, topic:papers