InterpretDL v0.7.0 Release

We release the version 0.7.0 of InterpretDL, with new features as follows:

• Examples are put into a separate directory examples/. Tutorials are still kept in the previous directory tutorials.
• A new explanation algorithm bidirectional_transformer is implemented.
• Documentation is improved.
• Fix some bugs.

We also would like to brag about ourselves that our paper with InterpretDL is accepted by Journal of Machine Learning Research (JMLR).

Xuhong Li, Haoyi Xiong, Xingjian Li, Xuanyu Wu, Zeyu Chen, and Dejing Dou. “InterpretDL: Explaining Deep Models in PaddlePaddle.” Journal of Machine Learning Research, 2022. https://jmlr.org/papers/v23/21-0738.html.

One survey paper is accepted by Knowledge and Information Systems (KAIS):

Xuhong Li, Haoyi Xiong, Xingjian Li, Xuanyu Wu, Xiao Zhang, Jiang Bian, and Dejing Dou. “Interpretable Deep Learning: Interpretations, Interpretability, Trustworthiness, and Beyond.” Knowledge and Information Systems, 2022, Springer. https://arxiv.org/abs/2103.10689.

And two research works got accepted by ECML'22 and Machine Learning Journal:

Xuhong Li, Haoyi Xiong, Siyu Huang, Shilei Ji, Dejing Dou. Cross-Model Consensus of Explanations and Beyond for Image Classification Models: An Empirical Study. ECML'22, Machine Learning Journal Track. https://arxiv.org/abs/2109.00707.

Xuhong Li, Haoyi Xiong, Yi Liu, Dingfu Zhou, Zeyu Chen, Yaqing Wang, and Dejing Dou. "Distilling ensemble of explanations for weakly-supervised pre-training of image segmentation models." Machine Learning (2022): 1-17. https://arxiv.org/abs/2207.03335.

We have also released a dataset containing 1.2M+ pseudo semantic segmentation images of ImageNet. Refer to PaddleSeg:PSSL for downloading the dataset and the pretrained models.